The video highlights the potential for looped LMs to improve parameter efficiency, which is crucial for resource-constrained environments.

This parameter efficiency can be leveraged to offer cost-effective AI voice agent solutions to businesses with limited budgets.

Develop pricing models that reflect the cost savings associated with using looped LMs, making AI voice agents more accessible to a wider range of businesses.


The looped architecture allows for dynamic reasoning and adaptation, which can be valuable for AI voice agents interacting with users in complex and unpredictable scenarios.

LLMs Don't Need More Parameters. They Need Loops.

This dynamic reasoning can lead to more natural and effective conversations with users.

Design AI voice agents that leverage the dynamic reasoning capabilities of looped LMs to adapt to user needs and preferences in real-time.


The ability to perform knowledge manipulation effectively is a key differentiator for looped LMs.

Focus on developing AI voice agents that can perform tasks requiring reasoning, problem-solving, and decision-making, rather than simple information retrieval.

Prioritize use cases that showcase the knowledge manipulation capabilities of AI voice agents, such as providing personalized recommendations, resolving complex customer inquiries, or automating decision-making processes.


Looped language models excel at knowledge manipulation (reasoning) but not knowledge storage.

Focus development efforts on multi-agent systems requiring complex reasoning and decision-making, rather than simple fact retrieval.

Prioritize R&D into integrating looped architectures into multi-agent orchestration systems.


Looping provides a third axis of scaling beyond model size and dataset size, particularly beneficial for smaller models.

Offers a competitive advantage in creating efficient and powerful AI voice agents that can run on resource-constrained devices.

Investigate the feasibility of deploying looped language models on edge devices for real-time voice agent applications.


The exit gate mechanism in looped models requires careful training to avoid 'reward hacking' where the model always exits at the same loop.

Understanding and mitigating reward hacking is crucial for the stability and effectiveness of looped language models in production.

Implement entropy regularization during training to encourage a uniform distribution of exit probabilities across loops.


Looped models offer flexibility in KV cache usage, with different strategies for training and inference.

Optimizing KV cache management is critical for improving the performance and efficiency of looped models, especially during inference.

Experiment with different KV cache strategies (exit loop only, averaging, first loop) to identify the optimal configuration for specific use cases.


The Oro model outperforms larger models (Gemma 3, QN3) on certain benchmarks, demonstrating the parameter efficiency of looped architectures.

Provides a strong selling point for AI voice agents built on looped models, highlighting their ability to deliver superior performance with fewer resources.

Benchmark AI voice agents built on looped models against existing solutions to quantify the performance gains and cost savings.


Looping can be injected into the pre-training pipeline, improving the base model's capabilities.

This allows for more efficient and capable models from the start, rather than relying on post-training techniques.

Incorporate looping mechanisms into the pre-training process for AI voice agents to enhance their reasoning and understanding abilities.


The speaker mentions the challenges of training looped models, including loss spikes and the need for constant monitoring.

Highlights the importance of robust infrastructure and skilled engineers for training and deploying looped language models.

Invest in robust monitoring and alerting systems to detect and address training issues in looped models.


The speaker draws an analogy between looping and how the brain learns, suggesting that it's a more efficient way to utilize existing parameters.

This analogy can be used to explain the benefits of looped models to potential clients in a more intuitive way.

Incorporate the brain analogy into marketing materials and sales pitches to highlight the efficiency and effectiveness of looped models.


The paper explores the impact of different numbers of loops, finding optimal performance at 3-4 loops for challenging benchmarks.

Provides a starting point for optimizing the number of loops in AI voice agents for specific tasks.

Experiment with different numbers of loops in AI voice agents to determine the optimal configuration for various use cases.


The speaker mentions the use of synthetic datasets for controlled experiments on knowledge storage and manipulation.

Synthetic datasets can be used to rigorously evaluate the capabilities of AI voice agents and identify areas for improvement.

Develop synthetic datasets to test the knowledge storage and manipulation abilities of AI voice agents.


The speaker mentions mixture of experts as a way to decouple compute from model size.

Combining mixture of experts with looped models could further improve efficiency and scalability.

Investigate the potential of combining mixture of experts with looped architectures for AI voice agents.


The speaker highlights the limitations of reasoning within the vocabulary space of a language model.

Suggests that exploring alternative reasoning spaces could lead to further improvements in AI voice agent performance.

Research techniques for reasoning outside of the vocabulary space, such as using external knowledge graphs or symbolic reasoning systems.


The video emphasizes the importance of pre-training for looped language models.

Proper pre-training is crucial for the success of looped models and should be a focus of development efforts.

Develop a comprehensive pre-training strategy for AI voice agents based on looped architectures.


The speaker mentions the potential of looped models for small language models that can fit on mobile devices.

This opens up opportunities for developing AI voice agents that can run locally on user devices, improving privacy and reducing latency.

Explore the feasibility of deploying looped models on mobile devices for real-time voice agent applications.


Existing clients are willing to pay significantly more for additional services, especially if they are automated and require minimal effort from the agency.

How I Upsell Automated AI SEO to My GoHighLevel Clients (No Extra Work)

Significant revenue potential from upselling existing customer base with AI-powered solutions.

Prioritize developing AI-powered upsell packages for existing clients.


The 'set and forget' nature of Paige is a key selling point for agencies, reducing the need for extensive SOPs, employee training, and ongoing support.

Reduces operational overhead and increases profitability by minimizing manual effort.

Focus on building AI agents that are highly autonomous and require minimal human intervention.


White-labeling is crucial for agency adoption, allowing them to present the AI solution as their own service.

Increases agency adoption and reduces client churn by reinforcing the agency's brand.

Ensure all AI voice agents and multi-agent systems are fully white-labelable.


Paige uses a data-driven approach to optimize Google Business Profile actions, automatically adjusting frequency based on algorithm changes.

Ensures optimal performance and reduces the need for manual monitoring and adjustments.

Implement a data-driven optimization engine that continuously learns and adapts to changing conditions.


Automated content creation, including images and videos, is a valuable feature that saves agencies time and effort.

Reduces content creation costs and increases client engagement.

Develop AI agents capable of generating high-quality, SEO-optimized content, including images and videos.


The platform provides an account manager feature that automates client communication, including onboarding, review reminders, and weekly summaries.

Improves client satisfaction and reduces churn by providing proactive and personalized communication.

Develop AI agents that can handle client communication, including onboarding, support, and progress updates.


The platform audits bad reviews and flags them with Google, increasing the likelihood of removal.

Improves the client's online reputation and increases customer trust.

Develop AI agents that can monitor and manage online reviews, including identifying and flagging potentially harmful or illegitimate reviews.


The system uses a 'Train Paige' feature to allow agencies to customize the AI's behavior and output based on client-specific requirements.

Ensures that the AI's output is aligned with the client's brand and messaging.

Implement a customization mechanism that allows agencies to fine-tune the AI's behavior and output.


The platform integrates with other tools, such as Google Drive and Box, to streamline the image upload process.

Reduces friction and improves the user experience.

Integrate AI agents with other popular tools and platforms to streamline workflows.


The platform leverages data science to identify the factors that are most correlated with ranking success.

Provides valuable insights that can be used to optimize the AI's performance.

Continuously analyze data to identify the factors that are most correlated with success and use these insights to improve the AI's performance.


The platform creates automated YouTube videos for clients, which can improve their search engine ranking and drive traffic to their website.

Increases brand visibility and drives traffic to the client's website.

Develop AI agents that can create and distribute videos on YouTube and other social media platforms.


The platform provides a FAQ widget that can be added to the client's website to educate LLMs about the business.

Improves the accuracy and relevance of AI-powered search results.

Develop tools that can help clients educate LLMs about their business.


The platform offers structured schema markup that can be added to the client's website to improve its search engine ranking.

Improves search engine visibility and drives traffic to the client's website.

Develop AI agents that can generate and implement structured schema markup.


The platform provides a churn prevention feature that identifies clients who are at risk of churning.

Reduces churn and increases customer lifetime value.

Develop AI agents that can identify and address the factors that contribute to churn.


The platform emphasizes the importance of educating local businesses about the value of GBP optimization.

Increases adoption of the platform and drives revenue.

Develop educational materials that can be used to educate local businesses about the value of AI-powered SEO solutions.


Skills are essentially instructions for an AI agent on how to do a specific process, can self-improve, and have human-in-the-loop.

How to build Claude Skills Better than 99% of People

Defines the core concept of skills, crucial for understanding the market and building appropriate solutions.

Prioritize building skills that allow for self-improvement and human-in-the-loop interaction.


Skills consist of a skill.md file (SOP) and reference files (text, assets, code scripts).

Highlights the modularity and extensibility of skills, informing the architecture of the multi-agent system.

Design a system that supports various reference file types and allows for easy integration of new ones.


Progressive disclosure is used to manage context: only metadata is stored in agent memory until the skill is triggered.

Addresses the context window limitations of LLMs, essential for scaling multi-agent systems.

Implement a progressive disclosure mechanism to efficiently manage context and allow agents to access thousands of skills.


Plugins are packaged sets of skills, commands, agents, and connectors, offering a higher level of abstraction.

Plugins provide a way to bundle and share functionality, potentially creating a marketplace for AI agents.

Explore the possibility of building and offering plugins to businesses, focusing on specific industry needs.


Skills can be customized from third-party providers or built from scratch.

Offers two distinct approaches to skill development, catering to different user needs and expertise levels.

Offer both pre-built skills and customization services to cater to different client needs.


Building good skills is like software engineering for AI agents, requiring UX considerations and context engineering.

Emphasizes the importance of a structured approach to skill development, ensuring quality and usability.

Develop a skill engineering methodology that incorporates UX principles and context management best practices.


The ideal step-by-step process and relevant knowledge sources should be defined before prompting to build a skill.

Highlights the importance of planning and preparation in skill development, leading to better outcomes.

Create templates and guides to help users define the process and gather relevant knowledge before building skills.


The skill.md file should focus on the core process, while additional information should be stored in reference files.

Promotes a clean and organized skill structure, improving maintainability and performance.

Enforce a clear separation of concerns between the skill.md file and reference files in the skill development process.


Instruct the skill to be automatically updated and improved when using the skill (self-learning).

Enables continuous improvement of skills, leading to better performance over time.

Implement a mechanism for skills to learn from user interactions and automatically update their knowledge base.


Monetization of skills is possible, similar to a new software layer.

Opens up new revenue streams for the AI agency.

Explore different monetization models for skills, such as subscription, usage-based pricing, or licensing.


Skills and plugins are relevant for any industry, business, department, and niche workflow.

Highlights the broad applicability of skills and plugins, expanding the potential market.

Target a wide range of industries and businesses with tailored skill and plugin solutions.


SAS businesses will start launching their own plugins with their specific functionalities.

Indicates a potential competitive landscape where existing software companies integrate AI agents.

Monitor the market for SAS plugins and develop strategies to differentiate the AI agency's offerings.


The presenter offers an AI accelerator with templates and blueprints for building skills.

Identifies a potential source of inspiration and learning for skill development.

Analyze the presenter's AI accelerator and incorporate relevant insights into the AI agency's skill development process.


Skills are becoming monetizable, like a new software layer.

Significant revenue potential for AI skill development and marketplaces.

Explore building and selling skills on existing marketplaces and consider creating a proprietary marketplace.


Skills are essentially instructions for an AI agent on how to do a specific process, sitting between isolated projects/custom GPTs and hardcoded automation platforms.

Defines the core value proposition of skills as a flexible and adaptable automation solution.

Position AI voice agents and multi-agent systems as 'skill-based' solutions, highlighting their adaptability and human-in-the-loop capabilities.


Provides a clear structure for designing and building effective skills.

Develop a skill template based on the skill.md + reference files structure, including best practices for each file type.


Progressive disclosure is used to manage context: only metadata is stored in agent memory until the skill is triggered, then the skill.md is loaded, and finally reference files are loaded as needed.

Enables agents to access thousands of skills without being overloaded with context.

Implement a progressive disclosure mechanism in multi-agent orchestration systems to efficiently manage context and scale skill usage.


Plugins are packaged sets of skills, commands, agents, and connectors, adding a layer of complexity and shareability.

Plugins provide a way to bundle and distribute skills for specific departments or use cases.

Explore creating plugins that bundle AI voice agents, multi-agent workflows, and knowledge graph integrations for specific industry verticals.


Three layers of skills/plugins are emerging: general, marketplace, and company-specific.

Identifies different levels of skill development and distribution.

Focus on building company-specific and customizable skills, while also exploring opportunities in skill marketplaces.


Skills can be built by either manually performing a task with an AI agent and saving it as a skill, or by instructing the agent on how to build the skill directly.

Offers two distinct approaches to skill creation, catering to different user preferences and skill complexity.

Develop tools and interfaces that support both skill creation methods, allowing users to choose the most appropriate approach for their needs.


Iterating on skills is crucial for improvement; skills are never finished and get better with use.

Emphasizes the importance of continuous improvement and feedback loops in skill development.

Implement mechanisms for users to provide feedback on skill performance and trigger updates, fostering continuous improvement.


Preparing good output examples has the biggest impact on performance.

Highlights the importance of providing high-quality training data for skills.

Prioritize the creation of comprehensive and diverse sets of output examples for each skill to improve accuracy and effectiveness.


Including human-in-the-loop steps with multiple variations/options improves UX and output quality.

Enhances user satisfaction and allows for better control over skill execution.

Design skills with built-in human-in-the-loop steps that offer multiple options for users to choose from, improving the overall user experience.


Instructing the skill to be automatically updated and improved when used makes it self-learning.

Enables skills to adapt and improve over time without manual intervention.

Implement self-learning mechanisms in skills, such as saving approved outputs as good examples and updating rules based on user feedback.


If a skill doesn't follow the process correctly, make changes to the skill.md. If additional information is needed, add a reference file. If it does something wrong that should never be done, add a rule or update the knowledge file.

Provides a clear framework for troubleshooting and improving skill performance.

Develop a debugging and optimization guide based on this framework to help users effectively improve their skills.


Skills can be shared as zip files or deployed through GitHub.

Provides methods for distributing and collaborating on skill development.

Support skill sharing via zip files and GitHub integration to facilitate collaboration and distribution.


The presenter has an AI Accelerator program with downloadable and customizable skills.

Identifies a potential source of pre-built skills and expertise.

Investigate the AI Accelerator program and assess the potential for leveraging its resources or partnering with the presenter.


Skills are relevant for any industry, business, department, and any niche workflow.

Highlights the broad applicability of skills across various domains.

Target a wide range of industries and use cases when marketing AI voice agents and multi-agent systems, emphasizing their versatility and adaptability.


Implement trigger words (e.g., 'Genesis', 'AIVA') to prevent accidental execution of voice commands.

IndyDevDan: Voice to Claude Code Terminal - Complete Blueprint

Critical for safety and usability of voice agents. Prevents unintended actions.

Integrate a configurable trigger word system into our voice agent architecture.


Leverage RealtimeSTT with faster-whisper for local, cost-effective speech-to-text.

Significantly reduces API costs associated with transcription. Offers various model sizes for speed/accuracy trade-offs.

Integrate RealtimeSTT/faster-whisper into our voice agent pipeline, offering configurable model options.


Utilize conversation persistence via YAML files to maintain context across interactions.

Enables more natural and coherent conversations with the AI agent. Improves user experience.

Implement a YAML-based conversation history management system for our voice agents.


Compress verbose Claude Code output using GPT-4.1-mini for natural-sounding TTS responses.

Improves the quality and clarity of voice responses. Makes the agent sound more conversational.

Integrate a response compression step using a suitable language model (GPT-4.1-mini or equivalent) before TTS.


Explore VoiceMode MCP for native Claude Code integration and potential offline capabilities.

Offers a more polished and potentially more efficient integration with Claude Code. Provides offline STT/TTS options.

Evaluate VoiceMode MCP as a potential alternative to the Claude Code CLI subprocess approach.


Address WSL2 microphone limitations by providing clear setup instructions or offering native Windows support.

Ensures a smooth user experience for users on WSL2. Reduces friction during setup.

Document WSL2 microphone setup (WSLg/PulseAudio) or offer a native Windows execution option.


Implement a system for managing and limiting Claude Code tool access via the `--allowedTools` parameter.

Enhances security and cost control by restricting the tools the AI agent can use.

Develop a mechanism for defining and enforcing allowed tools for our voice agents.


Offer a 'zero-cost' voice agent option by utilizing local STT (faster-whisper) and TTS (Kokoro or text-only).

Attracts users who are cost-sensitive or prefer local processing. Provides a competitive advantage.

Develop a configuration option that allows users to run the voice agent entirely locally, without API costs.


The 'Ears + Brain + Voice' architecture provides a simple, modular framework for building voice agents.

Provides a clear mental model for designing and developing voice agents. Facilitates modularity and maintainability.

Adopt the 'Ears + Brain + Voice' architecture as a guiding principle for our voice agent development.


Consider offering tiered pricing based on Claude Code usage, leveraging Claude Max plans.

Provides flexible pricing options for different user needs. Can increase revenue by catering to heavy users.

Develop pricing tiers that align with Claude Code usage and offer value-added services for higher tiers.


Explore integrating voice control into multi-agent orchestration systems to enable voice-driven workflows.

Allows users to interact with and control multi-agent systems using natural language. Opens up new possibilities for automation and collaboration.

Investigate the feasibility of integrating voice input into our multi-agent orchestration platform.


Dan's success with a 700-line Python script demonstrates the feasibility of building powerful voice agents with relatively simple code.

Encourages a lean and iterative approach to development. Reduces the barrier to entry for building voice agents.

Focus on building a core, functional voice agent with minimal code, then iterate and add features based on user feedback.


The ability to read and process files via voice commands (e.g., 'Sonnet, read some files in AI docs...') is a valuable feature.

Enables users to quickly access and analyze information using voice. Improves productivity.

Implement file reading and processing capabilities in our voice agents.


The video highlights the importance of 'plan-first' prompting for complex tasks.

Improves the quality and reliability of AI agent responses. Ensures that the agent considers the overall goal before taking action.

Incorporate plan-first prompting techniques into our voice agent workflows.


Monitor the evolution of Claude Code plugins and the MCP (Marketplace) as a potential distribution channel.

Provides access to a wider audience and potential revenue streams. Allows for easier integration with Claude Code.

Track the development of the Claude Code plugin ecosystem and consider publishing our voice agent as a plugin.


Claude Code's Plan Mode formalizes the 'understand-first, plan-second, build-third' workflow of senior engineers, emphasizing context building before execution.

Claude Code Plan Mode: The Senior Engineer's Workflow

Aligns with best practices for complex task decomposition and reduces errors by ensuring the agent has sufficient context.

Incorporate Plan Mode (Shift+Tab x2) as a standard step in our agent workflows for non-trivial tasks.


Workflow B (Plan then Write Spec then Execute) is recommended for mid-to-large features because agents are ephemeral and specs persist across sessions.

Ensures reproducibility, allows for review/iteration, and enables parallel agent execution from a single source of truth.

Prioritize spec-driven development and ensure all agents write plans to persistent storage (specs/) before execution.


The 'Plan IS the Prompt' - a well-crafted spec file is just another prompt, but a carefully crafted one that builds up the state space of the problem and solution.

Highlights the importance of high-quality, detailed specifications for driving agent behavior and achieving desired outcomes.

Invest more time in spec writing and develop tools/processes to improve spec quality.


Meta-prompting (writing a prompt that writes a prompt) is analogous to higher-order functions and enables recursive prompt composition.

Allows for complex task decomposition and orchestration by creating plans on plans.

Explore and implement meta-prompting techniques for complex multi-agent workflows.


The context window is the battlefield - Plan Mode is about loading the context window with the RIGHT information before making any mutations.

Emphasizes the importance of context management for agent performance and reliability.

Refine our context defense system and leverage Plan Mode as a native tool for context building.


Parallel agent execution from shared specs enables efficient task completion and diverse output generation.

Allows for rapid prototyping and exploration of different solutions based on a common plan.

Optimize our agent orchestration system to support parallel execution from shared specs.


The 'Correct me if I'm wrong' prompting technique reduces sycophancy bias in the model and encourages honest feedback.

Improves the reliability and accuracy of agent responses by reducing confirmation bias.

Incorporate this technique into our prompting guidelines for self-verification and plan validation.


The 'Think hard' keyword triggers Claude's extended thinking/reasoning model for deeper analysis.

Enhances the quality of agent-generated plans, PRDs, and complex specs.

Use 'think hard' in prompts for architecture planning, PRD generation, and complex spec creation.


Asking 'stupid' questions to the model in a zero-judgment environment helps validate assumptions and learn fast.

Encourages thoroughness and reduces the risk of errors by validating assumptions before building.

Promote a culture of asking clarifying questions and validating assumptions in our agent workflows.


Three essential directories for agentic coding: specs/, source/, and custom slash commands.

Provides a structured approach to organizing codebases for agentic development.

Adopt this directory structure for our agent projects to improve organization and maintainability.


The Infinite Agentic Loop pattern enables recursive prompt composition and parallel agent execution.

Allows for complex task decomposition and efficient resource utilization.

Adapt and integrate the Infinite Agentic Loop pattern into our agent orchestration system.


Modular output structure (separate HTML, CSS, and JS files) improves maintainability and realism.

Generates more realistic and maintainable code output.

Ensure our AI voice agents generate modular code structures for web-based interfaces.


The winning engineers and teams are setting up their codebases and tools, not for themselves, but for their agents.

Highlights the importance of designing codebases and tools that are optimized for agentic development.

Prioritize agent-centric design in our codebase and tool development efforts.


Dan recommends Workflow B (Plan, Write Spec, Execute) 4 out of 5 times for mid to large features.

Provides a guideline for choosing the appropriate workflow based on task complexity.

Develop a decision-making framework for selecting between Workflow A and Workflow B.


Custom slash commands (like `/prime` and `/infinite`) automate common tasks and load essential context.

Increases efficiency and reduces manual effort by automating repetitive tasks.

Expand our skills registry with more custom slash commands for common agentic coding tasks.


Gemini 2.5 Flash native audio is a core differentiator due to its direct audio processing, leading to more natural-sounding voice agents.

The Gemini Voice Gold Rush: How This Trend Is Being Turned Into Revenue (Step-By-Step AI Guide)

This technology provides a significant advantage over traditional speech-to-text-to-speech systems, resulting in superior voice AI quality.

Prioritize and highlight the native audio processing capabilities of AIVA in marketing materials and demos.


Businesses care more about outcomes (stopping missed calls, booking appointments, making money) than technical specifications.

Marketing efforts should focus on the tangible benefits and ROI for businesses, rather than technical features.

Overhaul marketing language for Talking Widget and ReceptionistAI to emphasize business outcomes, not tech jargon.


A pricing framework of $1,500 - $5,000 setup and $300 - $3,000/mo retainer is recommended for voice agent services.

Proper pricing is crucial for profitability and scalability. Undercharging can hinder growth.

Validate current pricing against this range and adjust as needed to ensure profitability and scalability.


Focusing on specific niches (dental offices, law firms, HVAC companies, etc.) allows for specialization and higher pricing.

Niche specialization enables targeted marketing and deeper understanding of customer needs, leading to increased sales.

Create vertical-specific landing pages for key niches (dental, legal, HVAC, real estate) with tailored messaging.


Key use cases for voice agents include customer service automation, appointment scheduling, lead qualification, after-hours support, and multilingual support.

These use cases demonstrate the value proposition of voice agents to potential clients.

Develop marketing materials and demos showcasing these specific use cases for each target niche.


Google AI Studio and Gemini 3 Pro offer no-code platforms for creating voice agents quickly and easily.

These tools lower the barrier to entry for building and deploying voice agents.

Evaluate Google AI Studio and Gemini 3 Pro for potential integration or inspiration in our agent development process.


Recurring revenue model (monthly service) is more valuable than one-time setup fees.

Recurring revenue provides a stable and predictable income stream.

Emphasize the monthly service aspect in sales pitches and marketing materials.


Businesses trust Google, lending credibility to voice agents built on Google's technology.

Leveraging the Google brand can increase trust and adoption of our voice agent solutions.

Highlight the use of Google's Gemini technology in marketing materials to build trust and credibility.


After-hours support is a strong selling point, offering 24/7 availability.

Provides a competitive advantage by offering continuous availability.

Emphasize the 24/7 capability of our voice agents in marketing materials, highlighting the benefit of always being available.


Multilingual support (70+ languages) is a premium feature for diverse markets.

Expands market reach and caters to diverse customer bases.

Position multilingual support as a premium add-on for businesses in diverse markets.


Building a community (e.g., Facebook group) can create a social proof engine and provide support for AI agency owners.

Community building fosters engagement, provides valuable feedback, and enhances brand loyalty.

Consider launching a Genesis/Sunaiva Facebook group for AI agency owners to foster community and provide support.


AI avatar content creation can be an effective marketing strategy.

AI avatars can create content 24/7, increasing reach and engagement.

Explore using an AI avatar for Sunaiva marketing content to increase content output and engagement.


ROI calculators can effectively demonstrate the value proposition of voice agents.

Quantifying the cost savings and revenue generation potential can increase sales conversions.

Build a simple ROI calculator showing potential cost savings and revenue increases for businesses using our voice agents.


Deep Think excels at complex simulations (power grid, ecosystem) showcasing architectural thinking and stability handling.

Gemini 3 Deep Think Is INCREDIBLE! World's Greatest AI Model EVER! (Tested)

This capability is directly applicable to building robust multi-agent orchestration systems that can handle complex real-world scenarios.

Explore using Deep Think for designing and simulating multi-agent system architectures, focusing on stability and resilience.


Deep Think achieves 84.6% on ARC-AGI-2, surpassing human baseline on novel visual logic puzzles.

Demonstrates genuine novel problem-solving ability, crucial for handling unforeseen challenges in AI agent interactions and knowledge graph reasoning.

Investigate using Deep Think for tasks requiring abstract reasoning and problem-solving within the multi-agent system and knowledge graph.


Deep Think can generate functional Minecraft clones and macOS clones from single prompts.

Highlights its ability to generate complex systems from high-level instructions, useful for rapid prototyping of AI agent behaviors and environments.

Experiment with Deep Think for generating initial code and configurations for AI agent environments and behaviors.


Deep Think's Codeforces ELO of 3,455 indicates elite competitive programming skills.

This level of coding proficiency can be leveraged for code review, architecture validation, and complex module design within the AI agency's projects.

Utilize Deep Think for automated code review and architecture validation of AI agent modules and orchestration systems.


Deep Think can convert hand-drawn sketches into 3D-printable models.

While not immediately core to the AI agency, this demonstrates a powerful capability for translating visual ideas into tangible prototypes, potentially useful for future product design workflows.

Explore potential applications of sketch-to-3D for internal prototyping and client demos.


Deep Think is accessed via Google AI Ultra subscription at ~$250/month.

Understanding the cost is crucial for budgeting and pricing AI agent services that leverage Deep Think's capabilities.

Factor the cost of Google AI Ultra into the pricing model for services that utilize Deep Think.


Deep Think API access is available via a waitlist.

API access is essential for integrating Deep Think into the AI agency's workflows and services.

Immediately sign up for the Deep Think API waitlist.


Deep Think excels at spotting subtle logical errors in PhD-level math papers.

This capability can be used to validate the logical consistency of knowledge graphs and identify potential errors in reasoning processes.

Explore using Deep Think to validate and refine the knowledge graph, ensuring logical consistency and accuracy.


The video showcases examples of landing page generation with dynamic animations and smooth scrolling.

This demonstrates Deep Think's ability to generate high-quality UI elements, which could be valuable for creating user interfaces for AI agents.

Explore using Deep Think for generating UI components and interfaces for AI agents and multi-agent systems.


Deep Think outperforms GPT-5 on 2D SVG generation.

This capability can be used to create custom visualizations and graphics for AI agent outputs and knowledge graph representations.

Evaluate Deep Think's SVG generation capabilities for creating visualizations of AI agent data and knowledge graph elements.


The video highlights community examples of photorealistic butterfly SVGs generated by Deep Think.

Demonstrates the potential for high-quality visual output, suggesting potential for creating visually appealing and informative representations of AI agent data.

Investigate prompting strategies and techniques for generating high-quality SVG visualizations with Deep Think.


The host emphasizes Deep Think's ability to handle complex engineering problems end-to-end.

This suggests Deep Think can be used for designing and implementing complex AI agent architectures and workflows.

Explore using Deep Think for designing and implementing complex AI agent architectures and workflows.


Deep Think's ability to design a decentralized power grid simulator showcases its architectural design and stability handling capabilities.

This demonstrates Deep Think's ability to handle complex, interconnected systems, which is relevant to multi-agent orchestration.

Investigate using Deep Think for designing and simulating the architecture of multi-agent systems, focusing on stability and resilience.


The video mentions that Deep Think can be used to generate a planet ecosystem simulation with thousands of AI-driven plants and animals.

This showcases Deep Think's ability to model complex interactions and dynamics, which is relevant to simulating multi-agent environments.

Explore using Deep Think to simulate complex multi-agent environments and interactions.


Claude-Mem achieves up to 95% token savings by avoiding re-explanation of context in subsequent sessions.

Claude Code With UNLIMITED Memory! Solves Claude's Memory Problem!

Significant reduction in operational costs for AI agents, enabling more complex tasks within token limits.

Quantify the token savings achievable with Genesis's memory systems and highlight this as a key selling point.


Claude-Mem's PostToolUse hook automatically captures tool usage, decisions, and observations for persistent memory.

Automated memory capture reduces manual effort and ensures comprehensive context retention for AI agents.

Implement automatic observation capture in Genesis's PostToolUse hook, currently only logging, not storing to memory systems.


Claude-Mem uses a 3-layer token-efficient retrieval system (search -> timeline -> get_observations) to minimize token usage.

Reduces token consumption during memory retrieval, enabling more efficient context injection.

Implement progressive disclosure in Bloodstream MCP (3-layer retrieval: search -> context -> full) to reduce token usage.


Claude-Mem injects relevant context before Claude processes each prompt using a UserPromptSubmit hook.

Per-prompt context injection allows for more dynamic and relevant memory retrieval.

Add UserPromptSubmit hook for per-prompt memory injection in Genesis, currently only SessionStart context loading.


Claude-Mem compresses observations via semantic summarization, achieving ~70% compression.

Reduces storage requirements, speeds up search, and lowers token costs.

Implement semantic compression before memory storage in Genesis to reduce raw observation size by 60-70%.


Claude-Mem exposes memory functionality through 5 MCP tools (search, timeline, get_observations, save_memory, __IMPORTANT).

Provides a structured and accessible interface for interacting with memory.

Study Claude-Mem's MCP tool design (search, timeline, get_observations, save_memory) for potential adoption in Genesis's Bloodstream.


The video demonstrates improved output quality with Claude-Mem, resulting in more precise and context-aware responses.

Highlights the value proposition of persistent memory for AI agents.

Showcase the improved output quality and reduced redundancy achieved with Genesis's memory systems in demos and marketing materials.


Claude-Mem's architecture includes lifecycle hooks (SessionStart, UserPromptSubmit, PostToolUse, Stop, SessionEnd) for managing memory.

Provides a structured approach to memory management throughout the agent's lifecycle.

Review and refine Genesis's existing hook system (observability_logger.py) to ensure comprehensive coverage of agent lifecycle events for memory capture.


The presenter injected catalogs of previous landing pages into memory, allowing Claude to remember design preferences.

Demonstrates the ability to personalize AI agent responses based on historical data.

Develop features that allow users to inject specific knowledge and preferences into Genesis's memory systems to personalize AI agent behavior.


Claude-Mem uses a local SQLite database for storage, which is not scalable for multi-agent systems.

Highlights the limitations of local storage for multi-agent systems.

Continue to leverage Genesis's distributed Elestio infrastructure for scalable memory storage.


Claude-Mem is open-source and free to use, providing a cost-effective solution for persistent memory.

Highlights the importance of competitive pricing and open-source options.

Evaluate Genesis's pricing strategy and consider offering a free tier or open-source components to attract users.


The video mentions the use of Bun runtime for managing the worker service.

Suggests alternative runtime environments for potential performance improvements.

Evaluate Bun runtime for potential performance improvements in Genesis's worker services.


The video highlights the ability to execute plans using sub-agents with memory context.

Reinforces the importance of multi-agent orchestration and memory sharing.

Continue to develop and enhance Genesis's multi-agent orchestration capabilities with a focus on seamless memory sharing between agents.


The presenter used the `/plugin` command to install Claude-Mem from the marketplace.

Demonstrates a streamlined installation process for plugins.

Explore the possibility of creating a plugin marketplace for Genesis to simplify the installation and management of extensions and integrations.


The video mentions OpenClaw integration for Telegram/Discord/Slack notifications.

Highlights the importance of integrating with popular communication platforms.

Explore integrations with popular communication platforms like Telegram, Discord, and Slack to provide users with real-time notifications and updates.


RLM (Recursive Language Models) using code execution and recursion can handle high complexity tasks outside of software engineering, acting as a reasoning breakthrough for AI agents.

Before You Build Another Agent, Understand This MIT Paper

Opens up new possibilities for AI agents in complex domains like legal analysis and policy review, previously inaccessible due to context limitations.

Prioritize R&D into RLM implementation, focusing on code execution and recursion within agent architectures.


Task complexity, specifically the complexity of documents (e.g., legal contracts with self-referencing clauses), significantly impacts LLM performance, leading to 'context rot' even before reaching context window limits.

Highlights the need to consider document structure and internal dependencies when designing AI agents for complex tasks.

Develop methods for analyzing and quantifying document complexity to optimize agent design and resource allocation.


Naive approaches like stuffing entire contexts into LLMs are expensive, slow, and can reduce reliability due to context rot and noise.

Reinforces the need for more sophisticated context management strategies beyond simply increasing context window size.

Avoid brute-force context stuffing in favor of more intelligent retrieval and processing techniques.


Summarization is lossy and can lead to agents drifting off-task, especially with complex tasks requiring nuanced understanding.

Suggests that summarization alone is insufficient for complex reasoning tasks and needs to be combined with other techniques.

Explore alternative or complementary methods to summarization for context reduction, such as dependency graph extraction.


RAG (Retrieval Augmented Generation) is brittle for complex tasks because it relies on semantic similarity and struggles with logical relationships needed for multi-hop reasoning.

Indicates that RAG is not a suitable solution for tasks requiring deep understanding of document structure and dependencies.

Investigate alternative retrieval methods that can capture logical relationships and dependencies within documents.


Modeling complex documents as dependency graphs (nodes as clauses/functions, edges as relationships) is a better mental model than treating them as linear text.

Provides a valuable framework for designing AI agents that can effectively reason over complex documents.

Develop tools and techniques for automatically extracting and representing dependency graphs from various document types.


RLM uses a 'ripple' (read, evaluate, print, loop) architecture with recursion, allowing intelligent search and synthesis of information within complex documents.

Offers a practical approach to overcoming context limitations and enabling multi-hop reasoning.

Implement the RLM architecture with a focus on the 'evaluate' step, which involves programmatic functions over data objects.


The 'evaluate' step in RLM can involve any programmatic function over the data object (slice, keyword match, etc.), enabling flexible context manipulation.

Highlights the importance of providing agents with a rich set of programmatic tools for interacting with data.

Develop a library of programmatic functions that can be used within the 'evaluate' step to perform various operations on data objects.


RLM can be cheaper and higher performance than other approaches (stuffing, RAG) for complex tasks, but requires high-performance models (GPT-5 level).

Provides a cost-benefit analysis for RLM implementation, highlighting the need for powerful underlying models.

Benchmark RLM performance with different model sizes to determine the optimal balance between cost and accuracy.


Guardrails are crucial for RLM to prevent infinite recursion and ensure cost-effectiveness. Limiting recursion depth and making workflows synchronous are initial strategies.

Emphasizes the importance of safety and control mechanisms in agentic systems.

Implement robust guardrails for RLM, including recursion depth limits, cost monitoring, and early termination mechanisms.


Asynchronous RLM (calling multiple sub-LLMs simultaneously) is an unexplored area with potential for significant performance gains, especially for large data rooms.

Suggests a promising avenue for future R&D in multi-agent orchestration.

Investigate asynchronous RLM architectures and their applicability to complex tasks involving large datasets.


RLM is not a one-size-fits-all solution; one-shot LLM or ripple without recursion may be better for small context or long context with low complexity.

Highlights the need for adaptive agent architectures that can dynamically select the most appropriate strategy based on task characteristics.

Develop a task classification system to determine when to apply RLM, one-shot LLM, or ripple without recursion.


RLM systems are more complex to monitor and implement in production, requiring careful consideration of observability and prompt engineering.

Highlights the challenges of deploying and maintaining RLM-based agents in real-world environments.

Invest in tooling and processes for monitoring RLM performance, debugging recursion loops, and optimizing prompts.


RLM can unlock tasks like legal analysis, policy review, and information synthesis on internal documents, providing significant value to businesses.

Identifies key target markets and use cases for RLM-based AI agents.

Focus marketing and sales efforts on these high-value use cases, emphasizing the benefits of RLM for complex document analysis.


Data provenance is crucial to mitigate hallucinations, even with RLM.

Reinforces the importance of data quality and traceability in AI agent development.

Implement robust data provenance tracking mechanisms for all RLM-based applications.


RLM (Recursive Language Models) using code execution and recursion significantly improves performance on complex tasks with long contexts compared to naive stuffing, summarization, or RAG.

Offers a more efficient and accurate approach to handling complex documents, potentially unlocking new use cases.

Prioritize research and development into RLM implementation for complex document analysis and knowledge synthesis tasks.


Task complexity, specifically the degree of internal self-referencing within documents (e.g., legal contracts, codebases), is a major factor limiting LLM performance, not just context length.

Highlights the need to focus on understanding and representing document structure, not just increasing context window size.

Develop methods for analyzing and quantifying the complexity of documents to inform agent design and task allocation.


Modeling complex documents as dependency graphs (nodes representing clauses/functions, edges representing relationships) is more effective than treating them as linear text for LLM reasoning.

Provides a better representation for enabling multi-hop reasoning and navigating complex relationships within documents.

Implement graph-based representations of documents within the memory/knowledge graph system to improve reasoning capabilities.


RLM's 'ripple' (read, evaluate, print, loop) architecture allows for intelligent search and synthesis of information within complex documents by programmatically operating on them.

Enables agents to selectively access and process relevant information, reducing context rot and improving accuracy.

Integrate a 'ripple'-like architecture into the multi-agent orchestration system to enable agents to interact programmatically with data assets.


RLM's recursive handoff of tasks to smaller models allows for more sophisticated multi-hop reasoning.

Overcomes the fragility of traditional multi-hop reasoning approaches.

Explore asynchronous, multi-agent recursion to process complex data rooms with multiple document types.


Guardrails are crucial for RLM to prevent infinite recursion and control costs.

Ensures the stability and cost-effectiveness of RLM-based systems.

Implement robust guardrails, including recursion depth limits and synchronous workflows initially, for RLM deployments.


RLM is not a one-size-fits-all solution; it's most effective for large context, complex retrieval, information synthesis, or research tasks.

Highlights the importance of task-specific agent design and selection.

Develop a task classification system to determine when RLM is the appropriate architecture for a given task.


The nature of the prompt significantly influences the evaluation process in RLM, requiring careful prompt engineering.

Emphasizes the need for optimized prompts to guide the agent's reasoning process.

Invest in prompt engineering research to develop effective prompts for RLM-based agents.


RLM can unlock tasks previously inaccessible to AI agents, such as legal analysis, policy review, and information synthesis on internal documents.

Expands the potential market for AI voice agents and multi-agent systems.

Target industries with complex document workflows (e.g., legal, finance, government) for RLM-based solutions.


RLM can be used for information synthesis on internal documents, which is a common pain point for companies with large volumes of unstructured data.

Provides a valuable service for businesses struggling to make sense of their internal data.

Develop a service offering focused on information synthesis for internal documents using RLM.


While RLM improves reasoning, it doesn't eliminate hallucinations, requiring data provenance measures.

Reinforces the importance of data quality and validation in AI agent development.

Integrate data provenance tracking and validation mechanisms into the AI voice agent platform.


The paper used GPT-5 and a 340B parameter coding model, suggesting high-performance models are needed for RLM.

Indicates a reliance on powerful LLMs for optimal RLM performance.

Focus development efforts on leveraging high-performance LLMs for RLM implementation.


RLM observability is more difficult, requiring advanced monitoring tools.

Highlights the need for specialized tools to track and debug RLM-based systems.

Invest in developing or acquiring advanced monitoring tools for RLM-based agents.


Trades (Home Services) are a prime target for AI voice agents due to labor shortages and high average ticket prices.

The Top 4 Industries to Sell Your AI Services in 2026

Validates ReceptionistAI's core market and provides strong ROI-based selling points.

Prioritize trades in ReceptionistAI marketing and sales efforts, emphasizing ROI based on booked jobs.


Real Estate agents need immediate lead response and systematic follow-up to increase conversion rates.

Highlights the value proposition of Talking Widget for real estate, particularly for after-hours lead capture.

Develop real estate-specific Talking Widget features and marketing materials focusing on lead response speed and CRM integration.


A revenue-share model with content creators and coaches can generate significant recurring revenue.

Offers a new revenue stream for Sunaiva and Talking Widget by partnering with agencies and leveraging their existing client base.

Pilot a revenue-share model with select agency partners for Talking Widget, focusing on content creators and coaches.


Professional services (lawyers, accountants) are willing to pay for automation that saves them time and prevents lost revenue.

Validates the potential of ReceptionistAI and Talking Widget in the professional services sector.

Develop targeted marketing materials for lawyers and accountants, emphasizing ROI based on reduced response times and improved client onboarding.


Database reactivation systems are a valuable upsell opportunity for all four industries.

Increases ARPA and provides additional value to existing clients.

Develop and market database reactivation features for ReceptionistAI and Talking Widget, highlighting the potential for increased revenue from past customers.


Lead qualification AI is a premium feature that can justify higher pricing.

Allows for tiered pricing and caters to clients who need more than just basic lead capture.

Enhance ReceptionistAI and Talking Widget with advanced lead qualification capabilities, such as scoring and routing based on pre-defined criteria.


ROI-based selling is crucial for convincing businesses to invest in AI automation.

Shifts the focus from cost to value, making it easier to close deals.

Train sales and marketing teams to emphasize the ROI of AI automation, quantifying the potential revenue gains and cost savings for each industry.


Speed of response is a key differentiator and value driver across all industries.

Highlights the importance of real-time lead capture and follow-up.

Optimize ReceptionistAI and Talking Widget for speed and responsiveness, ensuring minimal latency in lead capture and communication.


The $5K setup + $1K/mo retainer pricing model is validated for AI automation in trades.

Provides a benchmark for pricing ReceptionistAI and Talking Widget in the trades sector.

Evaluate current pricing for ReceptionistAI and Talking Widget in the trades sector, considering a potential increase to align with the validated market rate.


Building systems once for a coach/creator and then letting them sell access to their audience is a scalable model.

Reduces client acquisition costs and leverages existing customer bases.

Actively seek partnerships with coaches and creators who have large, engaged audiences in relevant industries.


Trades are experiencing labor shortages, making them desperate for automation solutions.

Creates a strong demand for ReceptionistAI's voice agent capabilities.

Focus marketing efforts on highlighting how ReceptionistAI can alleviate labor shortages in the trades by automating appointment booking and customer communication.


One happy client can lead to multiple referrals, especially in real estate and professional services.

Highlights the importance of customer satisfaction and building strong relationships.

Implement a referral program to incentivize existing clients in real estate and professional services to recommend ReceptionistAI and Talking Widget to their networks.


The key is understanding the pain points of each industry and tailoring the solution accordingly.

Ensures that the AI solutions are relevant and valuable to the target audience.

Conduct in-depth research on the specific pain points of each industry and develop customized solutions that address those needs.


Positioning against lost revenue is more effective than positioning against cost.

Frames the AI solution as an investment rather than an expense.

Refine marketing messaging to focus on the potential revenue gains and cost savings that businesses can achieve by using ReceptionistAI and Talking Widget.


Coding is becoming a commodity, shifting value to business problem understanding and AI agent management.

Google's AntiGravity Is a Gold Rush for Developers (If You Play It Right)

Validates Genesis's orchestrator-executor split and focus on business outcomes.

Emphasize business outcomes and problem-solving in all marketing materials.


One senior developer with AI can replace an entire team.

Validates the Genesis model of a senior human overseeing AI agents.

Use this narrative in pitch decks: 'Replace your dev team with one expert and AI.'


Agencies pivoting to AI-powered development will win by charging less and delivering faster.

Confirms the positioning of AgileAdapt and ReceptionistAI.

Continue to emphasize speed and cost savings in marketing these services.


There's a 6-12 month early adopter window before AI-built solutions become mainstream.

Creates urgency to ship products quickly and capture market share.

Prioritize product development and launch efforts to capitalize on this window.


The gym check-in system example highlights the value proposition: AI delivers solutions faster and cheaper.

Provides a concrete example for demonstrating the value of AI solutions.

Use this example (or similar ones) in sales pitches and marketing materials.


Focus on teaching 'building' skills (business, sales, communication) rather than coding.

Validates the focus on non-technical entrepreneurs as a target market.

Position Sunaiva/AgileAdapt training as the alternative to coding bootcamps.


Prompt engineering and AI agent management are becoming valuable skills.

Identifies a potential new product offering.

Consider developing an 'AI Agent Management' course or training program.


Coding bootcamps are in trouble as coding becomes commoditized.

Highlights a weakness in the traditional education system.

Target coding bootcamp graduates with messaging about upskilling in AI agent management.


Composio provides OAuth-as-a-Service for AI agents, solving a major blocker for production-ready agents needing access to 850+ SaaS apps.

Composio + Claude Code is Absolutely INSANE (FREE)

Enables Genesis agents to interact with real-world services like YouTube, LinkedIn, Gmail without complex OAuth implementation.

Sign up for Composio free tier and install as a Claude Code skill for immediate evaluation.


Composio offers a free tier (20,000 tool calls/month) and affordable paid tiers, making it a cost-effective alternative to Zapier for agent workflows.

Reduces operational costs for agent-driven workflows compared to existing Zapier integrations.

Compare Composio's pricing and features against our current Zapier usage to identify potential cost savings.


The video demonstrates using Composio to connect to YouTube and upload a video as a draft, showcasing its ability to automate content creation workflows.

Provides a concrete example of how Composio can be used to automate tasks within the content creation pipeline.

Prototype a Genesis agent that uses Composio to post to social media (LinkedIn, Twitter) for Sunaiva/ReceptionistAI marketing.


Composio connectors are framed as 'n8n blocks but native,' suggesting a potential replacement for n8n/Zapier in agent-driven workflows.

Offers a more direct and potentially efficient way to integrate agents with external services, bypassing the need for middleware.

Evaluate Composio vs current Zapier MCP for Genesis agent workflows, focusing on performance and ease of integration.


Composio allows embedding OAuth flows in applications, enabling end-users to connect their own accounts to services.

Solves the problem of how customers authenticate their CRM/calendar/email for Sunaiva/ReceptionistAI.

Integrate Composio OAuth flow into Sunaiva Talking Widget for customer calendar/CRM connections.


The host suggests an agent business model of replacing VAs and manual processes with AI agents, offering automation for $5-10K/month to companies spending $40K/month.

Validates the business model for ReceptionistAI and Talking Widget, demonstrating market demand for AI-powered automation.

Refine our pricing and marketing strategy to align with the value proposition of replacing manual processes with AI agents.


The video highlights the ability to give agents NPM packages as tools, expanding their capabilities beyond pre-built connectors.

Allows for highly customized agent functionality by leveraging the vast ecosystem of NPM packages.

Explore integrating NPM packages into Genesis agents to provide specialized functionalities like image processing or data manipulation.


The host proposes a YouTube thumbnail A/B testing agent as a compelling example of agent automation.

Demonstrates the potential for agents to optimize marketing content and improve performance metrics.

Explore the YouTube thumbnail A/B testing agent concept for Genesis marketing content.


Composio offers startup credits up to $25K for eligible startups.

Reduces the initial cost of adopting Composio and provides valuable resources for development and testing.

Submit an application for Composio startup credits to potentially receive up to $25K in free usage.


The Income Stream Surfers channel focuses on AI agent monetization and SaaS building, targeting developers and entrepreneurs.

Provides insights into the target audience and business models for AI agent services.

Analyze Income Stream Surfers' content and marketing strategies to identify best practices and potential partnership opportunities.


Composio supports multiple AI SDKs including Claude Agent SDK, OpenAI Agents SDK, Vercel AI SDK, and LangChain.

Provides flexibility in choosing the preferred AI framework for building agents.

Ensure compatibility and seamless integration with all supported AI SDKs for maximum developer flexibility.


The host used Claude Code + Composio to parse LinkedIn conversations and organize sponsor deal status automatically.

Demonstrates a practical application of AI agents for automating business processes.

Document and share this use case as a success story to showcase the value of Genesis agents.


Composio handles token refresh and API key management, simplifying the process of maintaining agent access to external services.

Reduces the operational overhead of managing API credentials and ensures continuous agent functionality.

Evaluate Composio's token refresh and API key management capabilities to ensure they meet our security and reliability requirements.


The video emphasizes the importance of building agents that are actually useful and solve real-world problems.

Reinforces the need to focus on practical applications and tangible benefits when developing Genesis agents.

Prioritize the development of agents that address specific pain points and deliver measurable value to our target customers.


The host mentions using Composio to access LinkedIn, Gmail, GitHub, Google Calendar, Notion, Supabase, Twitter, and Perplexity.

Highlights the breadth of services that can be integrated with AI agents using Composio.

Prioritize integrating these popular services into Genesis agents to meet the diverse needs of our customers.


Leverage Anthropic's Agent SDK for direct integration with Claude, bypassing the need for complex third-party integrations and maintaining a unified AI operating system.

I Turned Claude Code Into a Better OpenClaw

Simplifies development, reduces maintenance overhead, and ensures consistent performance between local and remote access.

Prioritize learning and utilizing the Anthropic Agent SDK for building AI voice agents and multi-agent systems.


Implement a multi-layered memory system (session-based, SQLite-based semantic/episodic memory, context injection) to manage context and knowledge effectively.

Improves the quality and relevance of AI agent responses by providing a richer understanding of user interactions and history.

Design and implement a multi-layered memory architecture for AI agents, incorporating session management, local database storage, and context injection techniques.


Offer users a customizable AI agent experience through a wizard-like onboarding process that allows them to select features, voice options, and memory systems.

Increases user satisfaction and adoption by providing a tailored AI agent experience that meets their specific needs and preferences.

Develop a user-friendly onboarding wizard for AI agents that allows users to customize features, voice options, and memory systems.


Explore using Groq for voice note processing due to its speed advantage over 11 Labs, while still offering 11 Labs for users who prioritize voice cloning.

Provides a balance between speed and personalization for voice note processing, catering to different user needs.

Evaluate and integrate Groq for voice note processing, while maintaining support for 11 Labs for voice cloning applications.


Consider supporting multiple LLMs (e.g., CodeX, Gemini) beyond Claude by abstracting the core logic and using a CLI-based interface.

Increases the flexibility and adaptability of the AI agent platform, allowing users to choose the LLM that best suits their needs.

Design the AI agent platform to be LLM-agnostic, supporting multiple LLMs through a CLI-based interface.


Offer a community or premium service for users seeking deeper support, advanced features, and pre-configured setups.

Creates a revenue stream and fosters a community around the AI agent platform.

Develop a premium service or community offering that provides enhanced support, advanced features, and pre-configured AI agent setups.


The creator emphasizes the importance of a unified AI operating system, where improvements to the core system benefit all connected applications (desktop, mobile, etc.).

Highlights the value of a centralized, well-maintained AI infrastructure for consistent performance and reduced redundancy.

Design the AI agency's offerings around a unified AI operating system, ensuring that improvements to the core system benefit all client applications.


The creator moved away from replicating open-source projects like OpenClaw due to the burden of maintaining separate codebases and the need for dual entry of skills.

Illustrates the challenges of maintaining multiple AI agent versions and the importance of a streamlined development process.

Avoid replicating existing open-source projects without a clear strategy for maintaining a unified codebase and avoiding dual entry of skills.


The video demonstrates the ability to handle multimodal inputs (video, images) through the AI agent.

Multimodal capabilities enhance the versatility and usefulness of AI agents for various business applications.

Incorporate multimodal input handling (video, images, audio) into AI agent offerings.


The creator uses Telegram as the communication medium, but emphasizes that any platform with an API can be used.

Highlights the flexibility of the AI agent platform and its potential for integration with various communication channels.

Design the AI agent platform to be compatible with multiple communication channels (e.g., Telegram, WhatsApp, Slack).


The creator uses a local SQLite database for memory storage, emphasizing its simplicity and cost-effectiveness.

Demonstrates a practical and affordable approach to memory management for AI agents.

Consider using SQLite as a cost-effective option for local memory storage in AI agent deployments.


The creator mentions using cron jobs for proactive AI agent behavior (e.g., reminders, scheduled tasks).

Proactive capabilities enhance the usefulness and engagement of AI agents.

Incorporate cron job functionality into AI agents to enable proactive behavior and scheduled tasks.


The creator emphasizes the importance of not breaking the terms of service of the underlying LLM provider (Anthropic).

Ensures the long-term viability and sustainability of the AI agent platform.

Prioritize compliance with the terms of service of all LLM providers used in AI agent offerings.


The creator is incentivized to add features to his setup, which is a strong selling point for his community.

Continuous improvement and feature additions are key to attracting and retaining users.

Establish a roadmap for continuous improvement and feature additions to AI agent offerings.


Anthropic's Agent SDK allows bridging to local Claude Code instances, enabling powerful personal assistants without relying on third-party APIs or cloud deployments.

This is a game changer because it allows businesses to leverage existing local AI infrastructure and avoid API costs while still benefiting from the model's intelligence.

Prioritize exploring and integrating Anthropic's Agent SDK for building local AI voice agents.


The creator uses Telegram as a communication medium, highlighting the potential for integrating AI agents into existing messaging platforms.

Integrating with popular messaging apps can significantly improve user accessibility and adoption of AI voice agents.

Explore integrating AI voice agents with popular messaging platforms like Telegram, WhatsApp, and Slack.


The system uses a multi-layered memory system (session ID, SQLite with semantic and episodic memory, context injection) to manage context and conversation history.

Robust memory management is crucial for creating engaging and context-aware AI voice agents.

Investigate and implement a multi-layered memory system for AI voice agents, including session management, semantic/episodic memory, and context injection.


The creator emphasizes the importance of a unified AI operating system where improvements to the core AI system benefit all connected interfaces (e.g., desktop and mobile).

A unified system reduces maintenance overhead and ensures consistent performance across different interfaces.

Design AI voice agent architecture with a unified core system that benefits all connected interfaces.


The mega prompt is designed to be self-building and customizable, interviewing the user to tailor the AI assistant to their specific needs.

Customization is key to creating effective AI agents for different business needs.

Develop a wizard-like interface or mega prompt system to guide users through the customization process of AI voice agents.


The creator uses Groq for voice notes due to its speed, but acknowledges 11 Labs as superior for voice cloning.

Choosing the right voice synthesis technology depends on the specific use case (speed vs. quality).

Evaluate and offer multiple voice synthesis options (e.g., Groq, 11 Labs) based on speed, quality, and voice cloning capabilities.


The creator offers a free mega prompt and a paid community for deeper dives and advanced features.

Freemium model can attract a wider audience and generate revenue through premium features and community access.

Implement a freemium revenue model, offering basic AI voice agent functionality for free and charging for advanced features, customization options, and community support.


The creator highlights the limitations of OpenClaw and the benefits of building a custom solution tailored to their specific needs.

Businesses often require custom AI solutions to address their unique challenges.

Emphasize the value of custom-built AI voice agents tailored to specific business needs and workflows.


The system can handle video, photo, and voice note inputs, demonstrating multimodal capabilities.

Multimodal input support enhances the versatility and usability of AI voice agents.

Develop AI voice agents with multimodal input capabilities, including video, photo, and voice note processing.


The creator mentions the ability to schedule tasks using cron jobs, highlighting the proactive capabilities of the AI assistant.

Proactive task scheduling can automate workflows and improve efficiency.

Integrate task scheduling capabilities into AI voice agents to enable proactive automation of business processes.


The creator emphasizes the importance of not breaking the terms of service when using language models.

Compliance with terms of service is crucial for ethical and sustainable AI development.

Ensure that AI voice agent development and deployment comply with the terms of service of all underlying language models and platforms.


The system uses SQLite for local memory storage, avoiding the need for cloud-based databases like Superbase or Convex.

Local memory storage can reduce costs and improve data privacy.

Explore using local databases like SQLite for memory storage to reduce costs and improve data privacy.


The creator mentions the ability to connect Telegram to WhatsApp, suggesting the potential for cross-platform integration.

Cross-platform integration can expand the reach and accessibility of AI voice agents.

Investigate and implement cross-platform integration capabilities for AI voice agents, allowing users to interact with them across different messaging platforms.


The creator is incentivized to add features to their setup, suggesting a continuous improvement and evolution process.

Continuous improvement is essential for maintaining a competitive edge in the rapidly evolving AI landscape.

Establish a continuous improvement process for AI voice agents, incorporating user feedback and new technological advancements.


The system can be adapted to use other language models with a command-line interface (CLI) like Codex or Gemini.

Flexibility in language model choice allows businesses to leverage the best model for their specific needs.

Design AI voice agent architecture to be modular and adaptable to different language models with CLI support.


Low-ticket products can be highly successful and generate millions in revenue, even in niche markets.

The $5/Day Ad Strategy Making Her Millions

Validates the potential for AI voice agents and multi-agent systems to support low-ticket product sales and customer service.

Prioritize developing AI solutions that cater to the low-ticket product market, focusing on efficiency and scalability.


Facebook/Meta ads have become simpler and more accessible, making it easier for businesses to start advertising with a small budget ($5/day).

AI voice agents can be integrated with Meta Ads to automate ad creation, targeting, and optimization, reducing the barrier to entry for businesses.

Explore integrations with Meta Ads API to enable AI-powered ad management and optimization for voice agents.


Directing ad traffic straight to the checkout page, bypassing traditional nurturing funnels, can significantly increase conversion rates.

AI voice agents should be designed to guide users directly to purchase options, streamlining the sales process and minimizing friction.

Develop AI voice agent scripts that prioritize direct calls to action and facilitate immediate purchases.


Upsells and order bumps are crucial for increasing Average Order Value (AOV) in the low-ticket space.

AI voice agents can be programmed to dynamically suggest relevant upsells and order bumps during the checkout process, maximizing revenue per transaction.

Implement AI-powered recommendation engines within voice agents to suggest personalized upsells and order bumps based on user behavior and purchase history.


Case studies and testimonials are powerful ad creatives, especially when they demonstrate tangible results (e.g., paying off a house with a $27 PDF).

AI voice agents can leverage customer success stories and testimonials to build trust and credibility, increasing conversion rates.

Integrate a system for collecting and managing customer testimonials and success stories, enabling AI voice agents to dynamically incorporate them into conversations.


Scaling requires new products and creatives to avoid customer fatigue and plateauing ad performance.

Multi-agent systems need to be designed to continuously generate new content, product offerings, and marketing strategies to maintain growth.

Develop AI-powered content generation tools to create new product descriptions, ad copy, and marketing materials for voice agents.


A high repeat customer rate (60% in this case) indicates strong customer loyalty and lifetime value.

AI voice agents should prioritize building long-term relationships with customers by providing excellent service and personalized recommendations.

Implement CRM integration to enable AI voice agents to track customer interactions, personalize recommendations, and proactively address customer needs.


Automating or outsourcing repetitive tasks is crucial for scaling a business.

Multi-agent orchestration systems should be designed to automate routine tasks, freeing up human agents to focus on more complex and strategic activities.

Identify repetitive tasks currently performed by human agents and develop AI-powered solutions to automate them.


The speaker uses SamCart for checkout pages due to its high conversion rates, emphasizing the importance of optimized checkout experiences.

AI voice agents need to seamlessly integrate with optimized checkout systems to ensure a smooth and efficient purchase process.

Evaluate and integrate with high-converting checkout platforms like SamCart to optimize the purchase experience for voice agent users.


The speaker emphasizes the importance of paying attention to key metrics like click-through rate, conversion rate, and AOV.

AI voice agent performance should be continuously monitored and optimized based on these metrics to maximize ROI.

Develop a comprehensive analytics dashboard to track key performance indicators (KPIs) for AI voice agents, enabling data-driven optimization.


Customers were upset they couldn't buy more during a sale because they already owned all the products.

Highlights the importance of continuous product development and offering new products to existing customers.

Develop a system to identify customers who have purchased all existing products and proactively offer them exclusive access to new releases or beta programs.


Friends and family are a good starting point to identify problems you can solve with a product.

AI can analyze social media and personal networks (with user permission) to identify potential product ideas based on common questions and requests.

Explore the feasibility of integrating AI with social media platforms to identify potential product opportunities based on user interactions and needs.


SKILL.md files enable deterministic workflow injection into Claude Code, replacing manual processes.

Claude Code + SKILL.md = Unlimited Custom Workflows (INSANE)

Validates the core concept of using skills for automation and provides a framework for creating them.

Prioritize skill creation and refinement based on high-impact workflows.


A four-step skill creation methodology involves finding an expert source, extracting the transcript, feeding transcript + assets to Claude Code, and generating the SKILL.md file.

Provides a concrete, repeatable process for creating new skills from existing knowledge sources.

Document and refine this methodology for internal use and potential client training.


Skills can integrate multiple external tools via API keys stored in .env files, enabling compound workflows.

Demonstrates the power of combining multiple AI and non-AI tools within a single skill.

Develop a library of pre-built skill components for common API integrations (e.g., image generation, web scraping, email).


Including example outputs in SKILL.md files can lead to repetitive and biased outputs.

Highlights a critical anti-pattern in skill design that can significantly reduce the quality and creativity of AI-generated outputs.

Audit existing skills and remove example outputs, focusing on providing methodology and constraints instead.


Project memory can be used to provide behavioral corrections to Claude Code, improving skill loading and execution.

Demonstrates a simple but effective technique for addressing agent behavior drift and ensuring consistent performance.

Implement a system for tracking and managing behavioral corrections in project memory.


Skills can significantly reduce the time required for specific tasks, leading to substantial ROI.

Provides a compelling value proposition for businesses considering AI automation.

Develop a ROI calculator to demonstrate the potential cost savings of using AI-powered skills.


Complex workflows can be broken down into multiple specialized skills, each optimized for a specific task.

Supports the multi-agent approach and highlights the benefits of modular skill design.

Design a framework for composing multiple skills into complex workflows.


Hamish expresses interest in releasing workflows to his audience, suggesting a potential market for curated skills.

Indicates a potential revenue stream through a skill marketplace or subscription service.

Explore the feasibility of creating a skill marketplace or sharing platform.


The video highlights the use of Gemini API for image generation, Gina.ai for web scraping, Bright Data for heavy scraping, and Resend for email outreach.

Identifies key tools and services that can be integrated into AI-powered skills.

Evaluate and integrate these tools into our skill development pipeline.


The host uses `claude --dangerously-skip-permissions` for rapid prototyping, but this is NOT recommended for production.

Highlights the importance of security and permissions management in production environments.

Establish clear guidelines for using and avoiding dangerous commands in development and production.


The host abandoned video editing skill creation due to perceived inauthenticity, indicating a potential limitation or challenge for AI in creative tasks.

Highlights the importance of considering the human element and potential limitations of AI in certain creative domains.

Focus on automating tasks where AI can provide clear value without compromising authenticity or quality.


The host uses internal web search to find company info/websites, suggesting a need for robust information retrieval capabilities.

Highlights the importance of integrating reliable information sources into AI-powered skills.

Investigate and integrate high-quality web search and knowledge graph capabilities into our platform.


The video showcases a specific use case for AI voice agents: thumbnail and title generation for YouTube videos.

Provides a concrete example of how AI voice agents can be used to automate content creation tasks.

Develop a skill specifically for thumbnail and title generation, targeting content creators as a potential customer segment.


The host's product, HarborSEO, focuses on keyword discovery, article writing, and backlink building, suggesting a potential area for skill development.

Identifies a specific domain (SEO) where AI-powered skills can provide significant value.

Explore the development of skills for keyword research, content optimization, and backlink analysis.


The host uses Learn by Leo YouTube channel as an expert source for viral content creation, highlighting the importance of leveraging existing knowledge.

Demonstrates the value of using expert knowledge to train and improve AI-powered skills.

Develop a system for identifying and integrating expert knowledge from various sources into our skill development process.


OpenClaw's multi-channel (WhatsApp, Telegram, Discord) integration for DevOps is a valuable feature for remote agent management and debugging.

OpenClaw + Antigravity Turns Your AI Into a Full Autonomous Engineering Team! Automate Your Code!

Enables mobile DevOps, remote debugging, and easier agent interaction.

Explore integrating similar multi-channel communication capabilities into Genesis, particularly for DevOps tasks.


The Antigravity Kit's role-based agents (UX designer, reviewer, debugger, builder) highlight the effectiveness of specialized agents.

Specialized agents improve output quality and efficiency.

Continue investing in and refining Genesis's specialized agent teams (FORGE, SURFACE, etc.).


The live demo building a CRM dashboard demonstrates the importance of an iterate-and-refine loop for improving agent output.

First-generation agent output is rarely perfect; refinement is crucial.

Ensure Genesis's verification gates and review processes are robust and efficient.


ClawHub, as a community skill marketplace, highlights the potential for a Genesis skill registry but also the associated security risks.

A skill marketplace can enhance extensibility but introduces supply chain vulnerabilities.

Explore building a Genesis skill registry with robust security measures (e.g., VirusTotal integration).


The video's warning about OAuth ToS changes emphasizes the risk of relying on free model access methods.

Free access methods are fragile and can be revoked.

Prioritize owning API keys and avoid relying on free OAuth methods for production.


OpenClaw's web dashboard for managing agents/sessions could inspire Genesis ops dashboard improvements.

Visual dashboards improve agent management and monitoring.

Evaluate and improve Genesis's operational dashboard for better agent management and session monitoring.


OpenClaw supports scheduling agent tasks via cron jobs, enabling autonomous timed execution.

Automated scheduling reduces the need for human prompting.

Ensure Genesis's auto-respawn and overnight evolution patterns are robust and easily configurable.


OpenClaw's architecture validates the orchestrator-executor pattern, mirroring Genesis's Claude + Gemini setup.

Confirms the effectiveness of the orchestrator-executor pattern for autonomous coding systems.

Continue refining and optimizing the Genesis orchestrator-executor architecture.


OpenClaw's security vulnerabilities highlight the importance of robust security measures.

Security is paramount for autonomous agent systems.

Continuously audit and improve Genesis's security measures, including Constitutional Guard and vulnerability scanning.


OpenClaw's basic session persistence is a weaker memory system compared to Genesis's Bloodstream.

Persistent memory is crucial for long-term agent learning and performance.

Continue investing in and improving Genesis's Bloodstream memory system.


OpenClaw's use of a single model at a time is less efficient than Genesis's rate-optimized Gemini swarm.

Multi-model orchestration improves efficiency and cost-effectiveness.

Continue optimizing Genesis's Gemini swarm for rate and performance.


The video demonstrates the potential for integrating AI voice agents with existing platforms like WhatsApp, Telegram, and Discord.

Expands the reach and accessibility of AI voice agents.

Explore integrating Genesis's AI voice agents with popular messaging platforms.


The video highlights the growing market interest in autonomous coding agent setups.

Indicates a strong demand for autonomous coding solutions.

Increase marketing and outreach efforts to capitalize on the growing market demand.


OpenClaw's lack of revenue infrastructure (Stripe, deployment, customer management) is a key differentiator for Genesis.

Genesis's revenue infrastructure provides a significant competitive advantage.

Highlight Genesis's revenue infrastructure in marketing materials and sales pitches.


The video highlights the importance of clear and concise documentation for setting up and using complex AI systems.

Good documentation improves user adoption and reduces support costs.

Ensure Genesis has comprehensive and user-friendly documentation.


The SEO Audit skill can autonomously implement SEO fixes like robots.txt, sitemap, meta descriptions, and Open Graph tags.

Claude Opus 4.6 Just Replaced Your Web Developer & SEO Team (Full Tutorial)

Automated SEO improvements can significantly boost website ranking and visibility, reducing manual effort.

Integrate the SEO Audit skill into our multi-agent orchestration system for automated website optimization.


The Front-end Design skill can handle theme changes, page creation, and responsive design adjustments.

Enables rapid prototyping and iteration of website designs without requiring manual coding.

Utilize the Front-end Design skill for creating and modifying landing pages for our AI voice agents.


Gitpage allows Claude Code to directly read/write HTML files via a Git repository, enabling AI-driven website management.

Facilitates a seamless workflow where AI agents can autonomously update and maintain website content.

Explore Gitpage as a platform for hosting and managing websites for our clients, enabling AI-powered content updates.


The CLAUDE.md file provides project-specific instructions, such as how to interact with a Git repository.

Allows for customized agent behavior based on the specific requirements of each project.

Develop a standardized CLAUDE.md template for website management, including instructions for Git repository interaction and SEO best practices.


The video demonstrates a 3-step agent pipeline: design prompt, page creation prompt, and SEO audit prompt.

Provides a structured approach for automating website development and optimization tasks.

Implement a similar multi-agent pipeline for our AI voice agent services, incorporating design, content creation, and SEO optimization steps.


The creator monetizes through a School community and affiliate links for Gitpage.

Offers potential revenue streams beyond direct service fees.

Explore creating a community platform for AI automation and consider affiliate partnerships with relevant tools and services.


Skills are global, meaning once installed, they are available across all working directories.

Simplifies agent configuration and ensures consistent functionality across different projects.

Design our AI agent architecture to support global skills, allowing for reusable functionalities across various client projects.


The video validates the market demand for AI-powered web development and SEO solutions.

Reinforces the viability of our AI agency's focus on automating web-related tasks.

Refine our marketing messaging to highlight the time and cost savings achieved through AI-powered web development and SEO.


The SEO Audit skill can identify and fix duplicate content issues.

Ensures website content is unique and avoids penalties from search engines.

Prioritize the duplicate content detection and resolution capabilities of the SEO Audit skill in our service offerings.


The demo includes updating the index page header menu to include a new services link.

Demonstrates the ability to modify website navigation elements.

Incorporate navigation update capabilities into our AI-powered website management services.


The SEO Audit skill suggests adding a blog and improving word count for better SEO.

Highlights the importance of content marketing for SEO.

Integrate content creation and optimization features into our AI-powered SEO services.


The SEO Audit skill can improve H1 tag structure and keyword optimization.

Improves on-page SEO for better search engine rankings.

Leverage the H1 tag and keyword optimization features of the SEO Audit skill to enhance website content.


The SEO Audit skill can generate sitemaps.

Improves website crawlability for search engines.

Ensure sitemap generation is a standard feature of our AI-powered SEO services.


The creator prompts Claude Code with natural language instructions.

Highlights the importance of natural language understanding in AI-powered web development.

Focus on improving the natural language processing capabilities of our AI agents to better understand and execute user instructions.


Context engineering, not prompt engineering, is key for complex agentic applications.

Highlights the shift from simple prompting to managing the entire context window for better agent performance.

Refocus R&D efforts from prompt engineering to context engineering techniques.


LLMs are stateless and require careful management of context for iterative tasks.

Emphasizes the need for external memory and state management solutions for agents.

Prioritize development of memory/knowledge graph systems for AI agents.


Context windows have limitations, and maximizing them doesn't always yield the best results (aim for 60-70% utilization).

Challenges the assumption that larger context windows are always better, suggesting efficiency is crucial.

Benchmark context window utilization vs. performance to optimize context size.


System prompts should define outcomes and broad approaches, avoiding overly prescriptive if-then logic.

Provides guidance on crafting effective system prompts for better agent behavior.

Develop best practices for system prompt engineering, emphasizing outcome-based instructions.


Tool descriptions need to be specific, detailed, and include schemas for inputs and outputs.

Ensures LLMs can effectively utilize tools within the agentic workflow.

Create a standardized format for tool descriptions, including input/output schemas.


RAG (Retrieval Augmented Generation) alone is insufficient for precise context engineering; MCP (Model Context Protocol) offers more control.

Suggests a move towards more sophisticated data retrieval methods for resources.

Explore and implement MCP or similar protocols for resource retrieval.


Agents can query for resources, like user details, only when needed, optimizing context window usage.

Demonstrates a strategy for efficient resource management within the context window.

Implement mechanisms for agents to dynamically request resources based on the current task.


Compaction (summarization) of resources is crucial for long-horizon agents to manage context size.

Provides a technique for reducing the size of resources within the context window.

Integrate summarization tools into the agent architecture for resource compaction.


Memory (key-value store) allows agents to store and retrieve intermediate results, reducing context clutter.

Enables agents to maintain state and access information without bloating the context window.

Develop a memory system for agents to store and retrieve intermediate results.


Complex agents may need to be decomposed into multiple specialized agents for better management and scalability.

Highlights the potential for multi-agent orchestration to handle complex tasks.

Design agent architecture with modularity in mind, allowing for decomposition into sub-agents.


Confluent Intelligence offers tools for building agents that work with streaming data.

Identifies a potential competitive tool for building data-driven AI agents.

Evaluate Confluent Intelligence for potential integration or competitive analysis.


The speaker encourages viewers to share their projects and engage in the comments.

Highlights the importance of community engagement and feedback.

Actively engage with the AI community to gather feedback and promote our solutions.


Confluent offers a free trial with a promo code.

Opportunity to explore a potential tool for free.

Sign up for the Confluent Intelligence free trial using the provided promo code.


The video focuses on building agents that take action in the real world, not just provide information.

Reinforces the importance of integrating tools and APIs for real-world applications.

Prioritize development of tools and API integrations for AI agents to enable real-world actions.


Iterative LLM calls and tool calls are common in agentic applications, requiring careful context management.

Highlights the need for robust context management strategies to support iterative agent behavior.

Develop context management strategies that can handle iterative LLM and tool calls efficiently.


Context engineering, not prompt engineering, is key for complex agentic applications due to context window limitations.

Highlights the need to focus on managing and optimizing the information provided to the LLM rather than just crafting the initial prompt.

Shift focus from prompt engineering to context engineering strategies, including data retrieval and summarization.


Optimizes LLM performance by allowing it to leverage its reasoning capabilities within defined boundaries.

Refine system prompt design to focus on desired outcomes and general guidelines rather than detailed instructions.


Tool descriptions should be specific, detailed, and include schemas for inputs and outputs.

Enables the LLM to effectively utilize tools by providing a clear understanding of their functionality and data requirements.

Develop a standardized format for tool descriptions that includes schemas and detailed explanations.


RAG (Retrieval Augmented Generation) alone is insufficient for precise context engineering; MCP (Model Context Protocol) and resource descriptions are needed.

Improves the relevance and efficiency of data retrieval by allowing the LLM to request specific resources.

Implement MCP or similar protocols to enable the LLM to query for specific resources as needed.


Compaction (summarization) and memory (key-value store) are crucial for managing context in long-horizon, iterative agent executions.

Prevents context window overload and maintains performance in long-running agents.

Integrate summarization techniques and a key-value store into the agent architecture for managing long-term context.


Agentic applications may decompose into multiple specialized agents for complex tasks.

Allows for modularity, scalability, and improved performance by distributing tasks across multiple agents.

Design agentic systems with a modular architecture that allows for the creation and integration of specialized sub-agents.


Iterative LLM calls can be used to determine which resources to retrieve based on the user message.

Optimizes resource usage by only retrieving relevant information.

Implement a mechanism for the LLM to request specific resources based on the user message before incorporating them into the context window.