Consider using user IDs instead of full user records in the context window and prompting the LLM to request more details if needed.

Reduces context window size and improves efficiency.

Implement a strategy for using user IDs and prompting the LLM to request additional user information when necessary.


Confluent Intelligence helps solve context problems for agents working with streaming data.

Highlights a potential partnership or competitive threat in the streaming data space.

Evaluate Confluent Intelligence and similar tools for potential integration or competitive analysis.


The optimal context window usage is around 60-70% of the maximum capacity.

Maximizes LLM performance by avoiding information overload.

Monitor context window usage and implement strategies to stay within the optimal range.


Offer compaction/summarization services as a premium feature for businesses struggling with large resource files.

Creates a direct revenue stream by addressing a key pain point in context management.

Develop and market a compaction/summarization service as a value-added offering.


Develop tools to help businesses engineer their system prompts effectively, striking a balance between vagueness and prescriptiveness.

Improves the overall performance and reliability of AI voice agents.

Create a system prompt engineering tool that guides users in defining outcomes and broad approaches.


Offer multi-agent orchestration as a solution for complex tasks that require long-horizon thinking and iterative execution.

Provides a more scalable and efficient approach to handling complex agentic applications.

Develop a multi-agent orchestration platform that allows businesses to decompose complex tasks into smaller, more manageable sub-agents.


Combine Agent Skills with Memory Files (CLAUDE.md) for persistent, automated workflows.

Skills.md + Claude.md Just Changed EVERYTHING (CLAUDE CODE)

Creates specialized virtual employees capable of complex tasks.

Prioritize development of robust skill and memory file integration within Genesis.


Use transcripts of expert videos to create domain-specific skill files.

Enables AI agents to learn from experts and apply knowledge consistently.

Automate the process of converting YouTube videos into Genesis skill files.


CLAUDE.md acts as a workflow router, mapping user commands to specific skill combinations.

Creates trigger-based automation within Claude Code.

Add explicit trigger mappings to skill definitions in Genesis.


Implement a '/clear' test to ensure workflow robustness and independence from conversation history.

Validates pre-compaction CTM protocol and seamless session respawn.

Verify Genesis workflows survive a clean slate using the '/clear' test.


Use skill files to teach Claude how to prompt external tools effectively (e.g., Nano Banana).

Enables seamless integration with various tools and services.

Apply this pattern to voice synthesis (Telnyx), lead generation (Instantly), and widget deployment.


Iteratively refine skills by analyzing successful outputs and encoding success patterns back into the skill.

Improves the quality and consistency of AI-generated outputs.

Formalize this iterative refinement process for content/marketing workflows.


Audit the official Agent Skills Directory on GitHub for adoptable skills.

Leverages existing skills and reduces development time.

Audit the repository at https://github.com/anthropics/claude-code-agent-skills.


Consider contributing Genesis-specific skills to the community Agent Skills Directory.

Increases visibility and adoption of Genesis.

Contribute skills related to voice agent config and MCP setup.


AI video editing is an advanced use case for future marketing content automation.

Could save significant time in producing demo videos.

Explore AI video editing workflows for ReceptionistAI/Talking Widget demo videos.


Create industry-specific AIVA skills by transcribing top sales trainers.

Enhances AIVA's personality and effectiveness in specific industries.

Transcribe top real estate/tradie/service business sales trainers and convert to AIVA personality skills.


Replicate the thumbnail/title/research pipeline for our own content marketing.

Automates and improves the quality of our content marketing efforts.

Build a similar workflow for our own content marketing needs.


Leverage the 'kung fu brain' effect by making Claude Code an expert in specific domains.

Improves the quality and consistency of AI-generated outputs in those domains.

Focus on creating skills that provide in-depth knowledge of specific domains.


The creator's preference for skill + memory over hooks suggests a more accessible approach for simpler workflows.

Confirms that hooks are a power-user feature.

Prioritize skill + memory pattern for simpler workflows while maintaining hook infrastructure for advanced users.


Vertical skills can be created from transcripts of YouTube videos.

Provides a scalable way to build domain-specific skills.

Take our Youtube Transcripts/ and convert the best ones into actionable `.claude/skills/` files.


Review Nano Banana MCP for image generation integration.

Ensures effective image generation capabilities.

Review Nano Banana MCP at `swarms/genesis-goose/documentation/docs/mcp/nano-banana-mcp.md`.


Anthropic's official guide on building Claude Skills emphasizes testing and iteration as crucial.

Anthropic Released Another Official Process

Highlights the importance of a robust testing framework and iterative development process for AI agent skills.

Prioritize building a comprehensive testing suite and iteration process into our AI agent development workflow.


Skill descriptions should answer 'What does the skill do?' and 'When should Claude use it?', including trigger and negative trigger phrases.

Improved skill selection and reduced unnecessary skill loading, leading to more efficient agent behavior.

Develop a template for skill descriptions that includes trigger and negative trigger phrases.


Skills should be structured with a three-level hierarchy: context (description), skill.md (workflow), and references (guidelines, scripts).

Improves context management and prevents information overload, leading to more efficient and accurate agent responses.

Implement a standardized three-level structure for all AI agent skills.


Validation gates are crucial for ensuring the quality and accuracy of AI agent outputs before proceeding to the next step.

Prevents errors and ensures that the AI agent produces high-quality results.

Incorporate validation gates into all AI agent workflows.


Error handling should be clearly defined within skills, including the error, cause, and steps to fix the problem.

Improves the robustness and reliability of AI agents by enabling them to handle errors gracefully.

Develop a standardized error handling framework for AI agent skills.


Skills can leverage external tools and scripts to extend their capabilities.

Allows for more complex and sophisticated AI agent workflows.

Explore and integrate relevant external tools and scripts into our AI agent skills.


Progressive disclosure is important for managing the context window and ensuring that the agent only has access to the information it needs at the time.

Reduces context window size and improves agent performance.

Implement progressive disclosure techniques in our AI agent skills.


References can be used to store domain-specific intelligence and expand the skill's knowledge base without polluting the context window.

Allows for more efficient and scalable knowledge management.

Utilize references to store domain-specific intelligence in our AI agent skills.


Cream.io offers AI agent skills for Claude Code, Cursor, and Windsurf, providing a payment solution.

Provides a potential revenue stream through integrating payment processing into AI agent workflows.

Investigate Cream.io's AI agent skills and evaluate their potential for integration into our AI agent platform.


The video mentions using Anti-gravity (likely a competitor to DALL-E or similar) for image generation, highlighting the importance of integrating with various tools.

Demonstrates the need for flexibility and integration with different tools to provide optimal solutions.

Ensure our multi-agent orchestration system can integrate with a variety of image generation and other relevant tools.


The channel uses AIABS Pro, a community with ready-to-use templates, as a way to support the channel.

Highlights the potential for a community-driven approach to revenue generation.

Explore building a community around our AI agency, offering templates and resources for a subscription or one-time fee.


The use case of generating a landing page highlights the potential for AI agents to automate web development tasks.

Identifies a specific application area for AI voice agents and multi-agent orchestration systems.

Focus on developing AI agent skills for web development tasks, such as landing page generation.


Claude Sonnet 5 achieves 82.1% on SWE-bench, surpassing human parity and outperforming GPT-5, making it a top coding model.

Claude Sonnet 5: Greatest AI Coding Model Ever! 1M Context, Cheap, & More! (Early Test)

This validates Sonnet 5 as a leading model for code generation and bug fixing.

Prioritize Sonnet 5 for coding tasks and explore its use in automated bug fixing workflows.


Sonnet 5's 'Dev Team' mode automatically spawns specialized sub-agents, mirroring the existing HIVE_STRUCTURE, potentially replacing manual agent orchestration.

Streamlines multi-agent workflows and reduces manual configuration.

Test 'Dev Team' mode for complex coding tasks, especially Talking Widget backend development, and evaluate its integration with HIVE_STRUCTURE.


Sonnet 5's 1M token context window allows processing the entire Talking Widget backend in a single context.

Enables cross-module refactoring, integration testing, and architecture reviews without context limitations.

Utilize the 1M context for comprehensive code analysis and refactoring tasks on the Talking Widget backend.


Sonnet 5 can generate complete landing pages with animations in a single prompt.

Accelerates UI/UX design and development.

Apply single-shot UI generation to Sunaiva landing page updates, ReceptionistAI demo pages, and Widget embed demos.


Sonnet 5 is significantly cheaper than Opus 4.6 ($3/$15 vs $15/$75 per MTok), offering comparable or better coding performance.

Reduces operational costs for AI agent development and deployment.

Switch coding tasks from Opus 4.6 to Sonnet 5, reserving Opus 4.6 for architecture and strategic orchestration, and update model selection logic in `core/gemini_rate_maximizer.py`.


Sonnet 5's built-in code execution and self-correction capabilities can automate debugging and verification.

Reduces human iteration cycles and improves code quality.

Integrate Sonnet 5's self-correction loop into the RWL story verification pipeline.


Unreleased 'Swarm' mode in Sonnet 5, with a chief commander and hierarchical sub-agents, mirrors Genesis's custom `multi_model_swarm.py`.

Potential to replace custom orchestration with a native, potentially more efficient, solution.

Monitor the release of Sonnet 5's 'Swarm' mode and evaluate its potential to replace the custom `multi_model_swarm.py` orchestration.


The upcoming 'Sonata' feature will add native image generation to Sonnet 5.

Enables automated generation of visual assets for marketing and UI/UX design.

When 'Sonata' is released, integrate it into the marketing pipeline to auto-generate social media visuals, ad creatives, and product screenshots.


Enterprise market share shows Anthropic leading with 40%, followed by OpenAI (27%) and Google (21%).

Provides competitive context for AI model selection and market positioning.

Monitor market share trends and adjust model usage and marketing strategies accordingly.


Claude Code's annualized revenue exceeded $1 billion within six months.

Demonstrates the growing market demand for AI coding tools.

Continue to invest in and expand the AI coding capabilities of Genesis.


The video demonstrates Sonnet 5's ability to generate complex interactive 3D applications in a single prompt.

This capability can dramatically reduce widget customization time for new verticals.

Prototype complete Talking Widget variants (real estate, plumbing, dental) as single-shot generations.


Claude Agent SDK now supports Sonnet 5 as default for Agent Teams, offering subagents with isolated context windows.

Potential to streamline agent orchestration and improve information retrieval.

Evaluate replacing manual Task tool orchestration with native SDK orchestration.


The cost reduction with Sonnet 5 allows for a shift in agent mix, favoring Sonnet 5 for coding tasks and Kimi/MiniMax for volume.

Significant cost savings without compromising performance.

Implement the new agent mix: Opus 4.6 (5%), Sonnet 5 (30%), Kimi/MiniMax (65%), and monitor cost savings.


Businesses are building custom mission control dashboards for OpenClaw to manage sub-agents, track costs, and monitor activity.

OpenClaw Built This $250K Mission Control in a Single Prompt

Highlights a clear market need for sophisticated multi-agent management tools.

Prioritize development of a user-friendly multi-agent orchestration platform with cost tracking and activity monitoring features.


Sub-agents are being used for specific tasks like analytics, content creation, and Shopify development.

Demonstrates the value of specialized AI agents for different business functions.

Develop a library of pre-built, customizable AI agents for common business tasks.


Mission control dashboards provide a centralized view of agent activity, task progress, and communication logs, improving transparency and control.

Centralized management is crucial for effective multi-agent system operation.

Design a comprehensive dashboard with real-time monitoring, task management, and communication logging capabilities.


Tracking agent costs (chat, decisions, cron jobs) is important for optimizing resource allocation and ROI.

Cost transparency is essential for justifying AI agent investments.

Implement detailed cost tracking and reporting features for each AI agent and task.


Memory is a weak point of OpenClaw, leading to the use of daily journals to track information and context.

Highlights the critical need for robust memory and knowledge management in AI agents.

Invest in developing advanced memory and knowledge graph systems for AI agents to improve long-term learning and performance.


One company has productized their mission control dashboard and is selling it for $99/month.

Validates the market demand for commercial multi-agent management solutions.

Explore different pricing models for the multi-agent orchestration platform, including subscription-based options.


The mission statement for the agents is debatable, but some users believe it helps them understand their purpose.

Agent alignment and goal setting might improve performance.

Experiment with different methods for defining agent roles and objectives, including mission statements and goal-oriented prompts.


Lovable is used to rebuild the mission control due to its better design, control, and hosting capabilities compared to OpenClaw's native interface.

Highlights the importance of a user-friendly and customizable interface for AI agent management.

Prioritize the design and development of a visually appealing and intuitive user interface for the multi-agent orchestration platform.


The rebuilt dashboard includes features like task management, content pipelines, cron job calendars, daily journals, and team management.

Provides a comprehensive list of features to include in a multi-agent management platform.

Incorporate these features into the multi-agent orchestration platform to provide a complete solution for managing AI agents.


Connecting the dashboard to Superbase allows for communication between OpenClaw and the dashboard.

Integration with existing tools and platforms is crucial for seamless workflow.

Develop APIs and integrations to connect the multi-agent orchestration platform with popular tools and platforms like Superbase, Slack, and Notion.


The creator is building a community of builders to teach them how to use AI to make money.

Community building and education can drive adoption and generate revenue.

Consider building a community around the multi-agent orchestration platform to provide support, education, and networking opportunities.


Alex Finn's mission control includes a task board, content pipeline, cron job calendar, daily journal, and a virtual office interface.

Demonstrates advanced use cases and potential features for a sophisticated multi-agent system.

Research and explore the feasibility of incorporating these advanced features into the multi-agent orchestration platform.


The ability to assign tasks to specific agents and track their progress is a key feature of successful mission controls.

Effective task delegation and monitoring are essential for multi-agent system performance.

Implement robust task assignment and tracking features in the multi-agent orchestration platform, allowing users to assign tasks to specific agents and monitor their progress in real-time.


The video highlights the evolution from basic agent management in Slack to sophisticated, custom-built mission control dashboards.

Illustrates the growing need for dedicated multi-agent management tools as AI agent adoption increases.

Position the multi-agent orchestration platform as a superior alternative to managing AI agents in general-purpose tools like Slack and Notion.


The use of Codec by the coding agent highlights the potential for specialized AI models within a multi-agent system.

Specialized models can improve the performance of agents in specific domains.

Explore the integration of specialized AI models into the multi-agent orchestration platform to enhance the capabilities of agents in different domains.


Businesses are building custom mission control dashboards to manage multiple AI agents and track their performance.

Highlights a growing need for sophisticated multi-agent management solutions.

Prioritize development of a robust multi-agent orchestration system with detailed monitoring and control features.


Sub-agents are being used for specific tasks like analytics, content creation, and development.

Validates the need for specialized AI agents that can be easily integrated into a larger system.

Develop a library of pre-built, specialized AI agents for common business functions.


Tracking agent costs (chat, decisions, cron) is important for understanding ROI.

Demonstrates the need for cost management features within multi-agent systems.

Implement detailed cost tracking and reporting features for each agent within the orchestration system.


One mission control is productized and sold for $99/month, including preset agents.

Validates the market demand for a commercial multi-agent management platform.

Explore different pricing models for the multi-agent orchestration system, including subscription options with pre-built agents.


Live activity feeds and detailed task views are crucial for monitoring agent progress and debugging issues.

Emphasizes the importance of transparency and control in multi-agent systems.

Develop a comprehensive activity logging and task management interface for the orchestration system.


Memory is a weak point of current OpenClaw implementations, leading to the use of daily journals.

Prioritize the development of a sophisticated memory/knowledge graph system for AI agents.


Lovable is used to rapidly prototype mission control dashboards due to its design capabilities and control over hosting/database.

Suggests a potential tool for rapid prototyping and deployment of AI agent interfaces.

Evaluate Lovable or similar low-code platforms for building custom interfaces for AI agents.


Connecting the mission control to external services like Slack is valuable.

Highlights the need for integrations with existing business tools.

Develop integrations with popular business communication and productivity platforms.


The video creator is running a successful agency leveraging AI, indicating a potential partnership or learning opportunity.

Provides a potential avenue for gaining insights and expanding market reach.

Reach out to the video creator to explore potential collaborations or knowledge sharing opportunities.


The use of a 'Chief of Staff' agent to manage sub-agents is a common pattern.

Suggests a hierarchical architecture for multi-agent systems.

Explore and implement a hierarchical agent architecture with a central coordinating agent.


The ability to pause, ping, and refresh agents is considered valuable.

Highlights the need for real-time control and monitoring of agents.

Implement features for pausing, restarting, and monitoring the status of individual agents.


Content creation is a significant use case for multi-agent systems.

Identifies a key application area for AI agents.

Develop specialized AI agents for content creation tasks, such as generating blog posts, social media updates, and video scripts.


Alex Finn's mission control includes a 'mission statement' for the agents.

Suggests a potential method for aligning agent behavior with business goals.

Investigate methods for incorporating mission statements or guiding principles into AI agent behavior.


AutoCloud uses a Kanban board UI for managing and visualizing multiple Claude Code sessions and sub-agents.

Auto Claude: AI Coding on Steroids! Claude Code Running Autonomous For Hours!

Provides a clear and organized way to monitor and control complex multi-agent workflows, improving user experience and debugging.

Incorporate a similar visual workflow management system (e.g., Kanban, Gantt chart) into our multi-agent orchestration platform.


AutoCloud supports fallback to different models (e.g., Sonnet 4.5) when Claude hits rate limits.

Ensures continuous operation and avoids disruptions due to model limitations, improving reliability and user satisfaction.

Implement a model fallback mechanism in our AI voice agent and multi-agent systems to handle rate limits or model unavailability.


AutoCloud integrates with Graphite's MCP server for memory management and uses embedding providers like OpenAI or O Lama (local).

Allows for persistent knowledge storage and retrieval, enabling agents to learn and improve over time. Local embedding options provide privacy and cost benefits.

Integrate support for multiple memory backends (e.g., vector databases, knowledge graphs) and embedding providers (OpenAI, local models) into our memory/knowledge graph system.


AutoCloud uses spec-driven development for higher quality outputs.

Improves the reliability and accuracy of generated code and other outputs.

Explore and implement spec-driven development techniques within our AI agent workflows to improve output quality.


AutoCloud includes features like isolated workspaces, self-validating QA, and AI-powered merges.

Enhances the robustness and security of the generated code and reduces the need for manual intervention.

Investigate and incorporate similar features (isolated workspaces, automated testing, AI-assisted code review) into our AI coding agent platform.


AutoCloud offers both a standalone app and a CLI for different user preferences.

Provides flexibility and caters to different user skill levels and workflows.

Offer multiple interfaces (GUI, CLI, API) for our AI agency's services to accommodate diverse user needs.


The video mentions a Patreon for access to a private Discord with free AI tool subscriptions.

Highlights a potential revenue model through community building and value-added subscriptions.

Explore creating a premium community with exclusive content, early access to features, and bundled AI tool subscriptions.


AutoCloud integrates with GitHub and GitLab for version control.

Streamlines the development process and enables collaboration.

Ensure seamless integration with popular version control systems (GitHub, GitLab, Bitbucket) in our AI coding agent platform.


The presenter had issues authenticating with Claude, requiring manual API key input.

Highlights potential friction points in the user onboarding process.

Prioritize a smooth and intuitive authentication process for all supported AI models, including clear instructions and troubleshooting guides.


AutoCloud can generate roadmaps for projects.

Helps users plan and prioritize tasks, improving project management.

Incorporate roadmap generation capabilities into our multi-agent orchestration system to assist users in planning and executing complex projects.


AutoCloud can generate ideas about code improvements, UI/UX, security, and performance.

Provides valuable insights and suggestions for improving the quality and security of the generated code.

Implement code analysis and suggestion features in our AI coding agent platform to identify potential improvements and vulnerabilities.


AutoCloud can identify and categorize security risks in the generated code.

Helps users identify and mitigate potential security vulnerabilities, improving the overall security of the generated code.

Integrate security scanning tools into our AI coding agent platform to automatically identify and report potential security risks.


The video highlights the importance of a sleek, modern, and intuitive UI for autonomous coding systems.

User experience is crucial for adoption and usability of complex AI tools.

Prioritize user-centered design principles when developing the UI for our AI agency's services.


The video mentions the ability to run 12 Claude Code agents simultaneously.

Demonstrates the potential for massive parallelization in multi-agent systems.

Design our multi-agent orchestration system to support a high degree of parallelization to maximize throughput and efficiency.


The presenter uses a descriptive prompt to define the desired application.

Highlights the importance of prompt engineering for achieving desired results.

Develop best practices and guidelines for prompt engineering to help users effectively communicate their requirements to our AI agents.


HTML output style enables generative UI, allowing agents to dynamically generate task-specific dashboards.

Engineers... Claude Code Output Styles Are Here. Don't Miss This TREND

Revolutionizes UI creation for agent interactions, reducing manual coding effort.

Implement HTML output style for dashboard generation in our AI voice agents and multi-agent systems.


YAML output format consistently produces higher quality agent responses compared to other formats.

Improves the reliability and accuracy of agent outputs, especially for complex tasks.

Utilize YAML output format for architecture, planning, and knowledge graph tasks within our AI agents.


Status lines are crucial for managing and monitoring multiple parallel Claude Code instances.

Enhances visibility and control over multi-agent systems, enabling efficient task orchestration.

Extend our existing status line implementation to include prompt history and agent naming for improved multi-agent tracking.


Hooks combined with state management (JSON files) provide a powerful agent tracking system.

Allows for persistent session data and context tracking, improving agent performance and coordination.

Implement session state management in our hooks to store prompt history per session in JSON files.


Information rate (information quality per response + information about agent activity) is the core metric for agent-human interaction.

Focuses development on maximizing the efficiency and effectiveness of agent communication.

Audit all agent interactions for information density improvements and optimize output styles and status lines accordingly.


TTS output style can be used for audio notifications when agents complete long-running tasks.

Provides real-time feedback on agent progress, improving user awareness and responsiveness.

Add TTS output style for multi-agent monitoring to provide audio notifications upon task completion.


Claude Code's value lies in being an unopinionated engineering primitive; avoid features that prescribe how to use models.

Maintains flexibility and control over agent behavior, preventing vendor lock-in and ensuring adaptability.

Ensure our custom layers (e.g., Gemini Rate Maximizer) remain the opinionated layer, while keeping Claude Code as the unopinionated primitive.


Conditional output styles can automatically select the appropriate format based on prompt keywords.

Streamlines agent interactions by automatically optimizing output format for different task types.

Build a conditional output style that auto-selects format based on prompt keywords (e.g., research -> GenUI, plan -> YAML).


Generating HTML report dashboards upon swarm completion can provide a visual summary of agent activities.

Enhances user understanding of multi-agent system outputs and facilitates data analysis.

Implement GenUI for agent reports, generating an HTML report dashboard upon every swarm completion.


Agent naming can be achieved using on-device LLMs like Ollama or simple hash-based methods.

Improves the clarity and organization of multi-agent systems by providing unique identifiers for each agent.

Implement agent naming in our status lines using either an Ollama-based model or a simple hash-based approach.


The video highlights the importance of context, model, and prompt as the three key elements of every agentic system.

Reinforces the fundamental principles of agent design, ensuring a solid foundation for development.

Continuously emphasize and optimize context, model, and prompt engineering in all our AI agent development efforts.


Dan's GenUI prompt includes consistent theming/styling, structured layout patterns, interactive elements potential, and task-specific visualizations.

Provides a blueprint for creating effective and user-friendly generative UIs.

Incorporate these elements into our GenUI implementation to create visually appealing and functional dashboards.


The video validates our existing approach to status lines and context tracking.

Confirms that our current development direction is aligned with industry best practices.

Continue refining our status line and context tracking implementations based on the insights from the video.


Leveraging output styles and status lines increases the information rate between engineer and agent, leading to faster context-switching and improved workflow.

Enhances productivity and efficiency in agent-driven development.

Prioritize the optimization of output styles and status lines to maximize the information rate in our agent interactions.


Dan's critique of Opus Plan Mode highlights the importance of maintaining full visibility into context, model, and prompt at every step.

Ensures transparency and control over agent behavior, preventing unintended consequences and facilitating debugging.

Review our usage of any similar planning modes to ensure we maintain full visibility into the underlying processes.


RLMs offer a significant performance boost (1400x) in reasoning tasks over large contexts compared to standard LLMs, at a manageable cost increase.

Forget LLMs: MIT's New Recursive Language Model (RLM) Explained

This validates the RLM approach for complex reasoning tasks, making it a viable alternative to simply scaling context windows.

Prioritize R&D into RLM-based multi-agent systems for business applications requiring deep reasoning over extensive data.


RLMs leverage a neuro-symbolic architecture, combining the strengths of LLMs (nuance, summarization, code generation) with symbolic systems (Python interpreter) for logical tasks and perfect recall.

This hybrid approach allows for more efficient and reliable processing of information.

Design AI voice agents that integrate LLMs with symbolic reasoning engines for tasks requiring both natural language understanding and precise data manipulation.


RLMs decompose complex tasks into smaller, manageable pieces through code generation (e.g., Python loops), enabling parallel processing by sub-LLMs.

This decomposition strategy is crucial for handling large datasets and complex reasoning problems.

Develop multi-agent orchestration systems that automatically decompose complex user requests into sub-tasks, assign them to specialized agents, and aggregate the results.


RLMs use a 'probe, decompose, recurse, aggregate' process, where the root LLM acts as an operating system managing access to an external environment (the prompt).

This architecture enables efficient processing of information without overwhelming the LLM's context window.

Implement a similar architecture in our multi-agent systems, where a central 'manager' agent orchestrates the work of specialized sub-agents.


RLMs verify results by making multiple recursive calls to different sub-LLMs, ensuring accuracy and reducing reliance on a single model's output.

This verification process enhances the reliability and trustworthiness of the system.

Incorporate a verification mechanism in our multi-agent systems, where multiple agents independently perform the same task and their results are compared for consistency.


The cost of RLM queries is predictable and manageable, even for million-token contexts, making it feasible for various applications.

This cost-effectiveness makes RLM a viable solution for businesses dealing with large amounts of data.

Develop pricing models for our AI voice agent and multi-agent systems based on the complexity of the task and the number of recursive calls required.


RLMs are deterministic, systematically working through a plan to ensure results, unlike RAG which relies on probabilistic retrieval.

This determinism provides higher reliability and accuracy for tasks requiring precise reasoning.

Position our RLM-based solutions as superior to RAG-based systems for applications where accuracy and reliability are paramount.


The RLM approach highlights the importance of a clear division of labor between the LLM (creative director) and the symbolic system (construction crew).

This division of labor enables more efficient and effective AI systems.

Design our AI voice agents and multi-agent systems with a clear separation of responsibilities between the LLM and the symbolic reasoning engine.


The community acknowledges the benefits of RLM but views it as an elegant implementation of existing concepts (AI agents using tools, breaking down problems).

This suggests that while the results are impressive, the core ideas are not entirely novel.

Focus on the practical benefits and performance improvements of our RLM-based solutions, rather than claiming groundbreaking innovation.


RLMs can be used to build systems that can reason over 10,000 documents, a task that standard LLMs struggle with.

This opens up new possibilities for AI applications in domains such as legal, financial, and scientific research.

Target industries with large document repositories and complex reasoning requirements as potential clients for our RLM-based solutions.


RLMs use code generation to probe the data and decompose the problem, allowing them to adapt to different data formats and task requirements.

This adaptability makes RLMs more versatile and robust than traditional LLMs.

Design our AI voice agents and multi-agent systems to be adaptable to different data formats and task requirements through code generation.


The paper highlights the limitations of simply increasing the context window size for LLMs, as it doesn't address the fundamental problem of context rot.

This reinforces the need for alternative architectures like RLM that can handle large contexts more effectively.

Avoid relying solely on increasing context window size in our AI voice agents and multi-agent systems, and instead explore RLM-based approaches.


The example of finding a beauty pageant winner in 10,000 documents demonstrates the ability of RLMs to perform multi-step reasoning tasks that are difficult for standard LLMs.

This showcases the potential of RLMs for complex information retrieval and analysis.

Develop demo applications showcasing the ability of our RLM-based solutions to perform multi-step reasoning tasks in specific industries.


Haiku 4.5 is 3x cheaper than Sonnet 4.5, making it ideal for high-volume, pattern-rich tasks.

Claude HAIKU 4.5 is LIGHT SPEED Agentic Coding... BUT can it BEAT Sonnet?

Significant cost savings in high-volume tasks like summarization and data extraction.

Prioritize Haiku 4.5 for suitable tasks across all AI voice agent and multi-agent systems.


Haiku 4.5 excels at file summarization, structured data extraction, and documentation scraping.

Allows for rapid and cost-effective processing of large volumes of information, improving agent efficiency.

Implement Haiku 4.5 for these tasks in our multi-agent orchestration systems.


Sonnet 4.5 is better suited for planning, architecture design, debugging, and complex multi-instruction following.

Ensures higher quality output for tasks requiring deep reasoning and complex logic.

Reserve Sonnet 4.5 for these tasks in our multi-agent orchestration systems.


The 'Sonnet plans, Haiku executes, Sonnet verifies' pattern optimizes cost and performance for refactoring and coding tasks.

Reduces overall cost while maintaining quality through verification.

Implement this pattern in our multi-agent systems for code-related tasks.


Haiku 4.5 can be used as a summarizer for agentic event streams in observability dashboards.

Provides real-time insights into agent behavior at a lower cost.

Integrate Haiku 4.5 into our observability dashboard for event summarization.


Consistent prompt structure (Purpose, Variables, Instructions, Workflow, Report) improves agent performance.

Leads to more predictable and reliable agent behavior.

Enforce this prompt structure across all agent deployments.


Multi-agent observability and orchestration is identified as a key area for future development.

Validates our strategic focus on multi-agent systems.

Continue investing in multi-agent orchestration and observability tools.


Model distillation means capabilities trickle down over time, allowing for cheaper models to handle previously complex tasks.

Allows for continuous cost optimization by shifting tasks to cheaper models as they improve.

Regularly re-evaluate model tier assignments for each task type.


The three dials of agent engineering are Performance, Speed, and Cost.

Provides a framework for optimizing agent deployments based on specific business needs.

Use these dials to guide model selection and parameter tuning for each agent.


The video validates the model stack approach (weak/base/strong) for agentic systems.

Confirms the effectiveness of our current model stack architecture.

Continue to refine and optimize our model stack based on performance data.


Haiku 4.5's limitations include complex multi-instruction following and deep planning.

Highlights the need for careful task assignment based on model capabilities.

Avoid assigning complex planning or multi-step tasks to Haiku 4.5.


Cache hits on repeated Haiku prompts can further reduce costs.

Encourages the design of systems that leverage repeated prompts for cost efficiency.

Design prompts to maximize cache hit rates for Haiku 4.5.


Regex search in the observability dashboard event stream is a useful addition for debugging.

Improves the efficiency of debugging and troubleshooting agent behavior.

Implement regex search in our observability dashboard event stream.


Low AOV ($90) with high volume (11,000+ orders) suggests a market for affordable, accessible AI voice agent solutions.

My First $1M Month Selling Digital Products (5 Things I Did Right)

Validates the potential for a high-volume, low-price AI agent offering.

Focus on creating affordable AI agent solutions with a freemium or low-cost entry point to attract a large customer base.


Emphasis on 'elimination' suggests a need for streamlined, easy-to-use AI agent platforms that minimize user effort.

Highlights the importance of user-friendly design and automation in AI agent solutions.

Prioritize intuitive interfaces and automated workflows in AI agent development to reduce user friction.


Focus on skill acquisition highlights the need for comprehensive training and support for AI agent users.

Demonstrates the importance of user education and ongoing support for successful AI agent adoption.

Develop robust training materials, tutorials, and support channels to help users master AI agent skills.


Success with AI-driven ads (ROAS of 2.5) indicates the potential for AI to optimize marketing and sales for AI agent solutions.

Validates the use of AI in marketing and sales to drive adoption of AI agent solutions.

Integrate AI-powered marketing tools and strategies to optimize ad campaigns and sales funnels for AI agent offerings.


The 'Viral Product Builder' tool demonstrates the value of structured frameworks and templates for product creation.

Highlights the potential for AI to assist in the creation and customization of AI agent solutions.

Develop AI-powered tools that provide templates, frameworks, and suggestions for building and customizing AI agents for specific business needs.


The speaker's journey from $63 to $1M+ highlights the importance of long-term commitment and iteration.

Emphasizes the need for continuous improvement and adaptation in the AI agent market.

Establish a culture of continuous learning, experimentation, and iteration in AI agent development and deployment.


The mention of shedding negative customers suggests the importance of identifying and focusing on ideal customer profiles for AI agent solutions.

Highlights the need to target specific customer segments with tailored AI agent offerings.

Develop detailed customer personas and tailor AI agent solutions to meet the specific needs of those target segments.


The speaker's focus on character and stewardship suggests a need for ethical considerations in AI agent development and deployment.

Emphasizes the importance of responsible AI practices and building trust with customers.

Incorporate ethical guidelines and transparency into AI agent development and deployment processes.


The speaker's success with digital products highlights the potential for packaging AI agent capabilities into easily consumable, productized offerings.

Validates the productization of AI agent solutions for wider adoption.

Develop packaged AI agent solutions that address specific business needs and are easy to implement and manage.


The speaker's emphasis on mindset and goal setting suggests the importance of clear vision and strategic planning for AI agency growth.

Highlights the need for a strong strategic foundation for AI agency success.

Develop a clear vision, mission, and strategic plan for the AI agency, including specific goals and milestones.


The speaker's lean team structure suggests the importance of automation and efficiency in AI agency operations.

Highlights the need for streamlined processes and automation to maximize productivity.

Implement automation tools and workflows to streamline AI agency operations and reduce manual effort.


The speaker uses SamCart, indicating a need for seamless integration with popular e-commerce and payment platforms.

Highlights the importance of compatibility with existing business tools and infrastructure.

Ensure seamless integration with popular e-commerce platforms, payment gateways, and CRM systems.


The speaker's success with a low-ticket business model suggests a potential entry point for AI voice agents could be simple, task-specific agents.

Highlights the potential for a high-volume, low-price AI agent offering.

Develop simple, task-specific AI voice agents that can be offered at a low price point to attract a large customer base. Examples: appointment scheduling, basic customer service Q&A.


Local hosting is strongly preferred for security, performance, and integration, but doesn't require expensive hardware to start.

100 Hours of OpenClaw Lessons in 35 Minutes

Validates the strategy of starting with accessible hardware and scaling up as needed.

Emphasize the accessibility of local hosting on basic hardware in marketing materials.


Anthropic Opus 4.6 is considered the best 'brain' model, while specialized models are used for specific tasks (coding, web search, social media).

Confirms the effectiveness of a brain-and-muscles architecture for AI agents.

Continue to optimize the brain-and-muscles architecture with Claude Opus and specialized models.


Reverse prompting (asking the AI what to do) is a key principle for maximizing OpenClaw's effectiveness.

Highlights the importance of AI agent proactivity and self-direction.

Incorporate reverse prompting more explicitly into agent workflows and training materials.


OpenClaw uses a self-improvement loop where failures trigger the creation of new skills/tools.

Validates the importance of automated self-improvement for AI agent evolution.

Continue to refine and automate the Alpha Evolve cycle for Genesis agents.


Discord channels are used to create structured workflows for AI agents (alerts -> research -> scripts).

Demonstrates the value of structured communication channels for multi-agent orchestration.

Explore ways to enhance the Hive team structure with more granular communication channels.


Security is paramount, especially regarding admin access and exposure to public inputs.

Highlights the critical need for robust security measures in AI agent deployments.

Prioritize security hardening for Sunaiva Talking Widget and all AI agent deployments.


MiniMax 2.5 running locally is sufficient for coding tasks.

Provides a cost-effective solution for coding tasks.

Explore moving coding tasks to local MiniMax 2.5 on AIVA's Mac Mini.


Creator Buddy's success ($300K/yr) demonstrates the potential of AI agents to proactively build revenue-generating features.

Provides a model for how Genesis should operate to generate revenue.

Focus on using Genesis to autonomously build and deploy revenue-generating features.


An approvals queue is needed for outputs requiring human review (tweets, thumbnails, scripts).

Identifies a key feature for Sunaiva Talking Widget and other AI agent applications.

Add an approvals queue to the Sunaiva Talking Widget dashboard.


Morning briefs (personalized daily reports) are a valuable use case for AI agents.

Highlights a practical application for AI agents that can improve user productivity.

Implement daily morning briefs for Kinan via AIVA, including KG updates, agent status, revenue metrics, and blockers.


OpenAI is openly supportive of OpenClaw, while Anthropic is less so.

Informs model selection and partnership strategies.

Prioritize OpenAI models and partnerships, while monitoring Anthropic's stance.


OpenClaw remembers everything about the user (preferences, goals, career, personal details) and improves based on every interaction.

Highlights the importance of comprehensive and persistent memory for AI agent effectiveness.

Continue to develop and enhance the Bloodstream Memory system.


The video emphasizes treating OpenClaw as a super-intelligent employee, not manually editing config files.

Reinforces the importance of abstraction and letting the AI handle implementation details.

Ensure that Genesis agents are designed to be managed through high-level commands, not manual configuration.


Firebase Agent Skills significantly reduce token usage when using AI for Firebase development.

Lower token costs directly impact the profitability of AI-driven development, especially at scale.

Quantify the potential token cost savings for typical Firebase development tasks and use this as a key selling point.


Agent Skills improve the quality and security of AI-generated Firebase code, reducing errors and vulnerabilities.

Higher quality code reduces debugging time and the risk of security breaches, leading to more reliable applications.

Develop case studies demonstrating the improved code quality and security achieved with Agent Skills.


Agent Skills lower the barrier to entry for Firebase development, enabling non-experts to build complex applications.

This expands the potential market for AI-powered development tools to include agencies, no-code builders, and small startups.

Target marketing efforts towards these non-expert user groups, highlighting the ease of use and reduced learning curve.


Agent Skills use progressive disclosure, loading only the necessary information for a specific task.

This makes AI interactions more efficient and reduces token usage.

Incorporate progressive disclosure techniques into our multi-agent orchestration systems to optimize resource utilization.


The Firebase MCP server, when paired with Agent Skills, provides direct programmatic access to Firebase projects.

This enables a full agentic Firebase workflow, where AI can write code and interact with the project directly.

Explore integrating similar direct access capabilities into our AI voice agents to allow them to directly manage and interact with business systems.


The video mentions specific Firebase skills: Basics, Auth, Firestore, Hosting, and AI Logic.

These skills represent key areas where AI can assist in Firebase development.

Analyze these skills to identify common patterns and best practices for AI-assisted development.


The speaker emphasizes the importance of joining the AI Profit Boardroom to stay ahead of the curve.

This suggests a strong demand for practical, hands-on AI training and implementation.

Offer similar training and implementation services to complement our AI agency offerings.


Agent Skills are open source on GitHub, allowing for customization and contribution.

This fosters community involvement and allows for continuous improvement of the skills.

Consider open-sourcing parts of our AI agency's tools and systems to encourage community contributions and gain valuable feedback.


The video highlights the problem of AI hallucinating code and generating incorrect security rules.

This underscores the importance of ensuring the accuracy and reliability of AI-generated code.

Implement robust testing and validation procedures to minimize the risk of AI-generated errors.


The speaker encourages viewers to drop a comment about which skill they will try first.

This demonstrates the importance of engaging with users and gathering feedback.

Actively solicit feedback from users of our AI voice agents and multi-agent systems to identify areas for improvement.


The video promotes a free AI course and access to a community of AI builders.

This highlights the value of providing free resources and community support to attract and retain users.

Offer free introductory courses or resources to attract potential clients and build a community around our AI agency.


The video was published in 2026, indicating a future trend towards AI-assisted development.

This reinforces the long-term potential of AI in software development.

Continue to invest in research and development of AI-powered tools and systems for software development.


The video mentions integration with Gemini, CLI, Cursor, and Claude Code.

These tools are popular among developers and represent potential integration partners.

Prioritize integration with these tools to ensure compatibility and maximize user adoption.


The video uses the example of setting up authentication and a database for a member portal.

This is a common use case for Firebase and represents a potential target market.

Develop solutions specifically tailored to building member portals and other similar applications.


The speaker is a digital avatar of Julian Goldie, CEO of an SEO agency.

This highlights the growing use of AI avatars for content creation and marketing.

Explore the use of AI avatars for our own marketing and communication efforts.


Gemini 3 Flash offers a superior speed/cost/quality ratio for parallel agentic work compared to Gemini 3 Pro.

Gemini 3 Flash should NOT EXIST. TOP 2% Engineering. /PLAN 2026

Significantly reduces the cost of running multi-agent systems while maintaining acceptable quality for tasks where outputs are validated.

Prioritize integration of Gemini 3 Flash into multi-agent orchestration systems for tasks like iterative development and exploration.


The 'Plan-Driven Development' methodology, using Claude Code's `/plan` feature, is a key component of 'Top 2% Engineering' and should be adopted.

Improves the reliability and quality of agentic code generation by ensuring a structured planning phase before execution.

Implement a similar planning feature in our multi-agent systems, potentially integrating it with a knowledge graph to provide context.


A hybrid workflow using Claude Opus for orchestration and Gemini 3 Flash for speed-critical parallel execution is optimal.

Leverages the strengths of both models, maximizing efficiency and quality in agentic workflows.

Design our multi-agent orchestration system to support a hybrid workflow, allowing agents to dynamically switch between Claude Opus and Gemini 3 Flash based on task requirements.


Trust in agentic systems is the key challenge in 2026, shifting focus from model capability to building reliable pipelines.

Highlights the importance of validation and monitoring in agentic systems, rather than solely relying on model accuracy.

Invest in developing robust validation pipelines for agent outputs, including automated testing and human-in-the-loop review processes.


Context engineering, prompt engineering, and building reusable agentic systems are crucial skills for leveraging AI models effectively.

Emphasizes the importance of human expertise in guiding and managing AI agents to achieve optimal results.

Develop internal training programs and best practices for context engineering and prompt engineering, and create a library of reusable agentic components.


Gemini 3 Flash's lower cost enables running a significantly larger number of agents in parallel (5:1 ratio vs. Pro).

Allows for more extensive exploration of solutions and faster completion of tasks through parallel processing.

Refactor pricing models to reflect cost savings and encourage clients to adopt larger, parallel agent swarms.


Flash's 'Dynamic Thinking' mode adds reasoning capabilities at minimal cost and should be enabled by default for coding tasks.

Improves the quality and reliability of code generated by agents by enabling them to reason about the task before execution.

Ensure that the 'thinking_level' parameter is set to 'high' by default when using Gemini 3 Flash for coding tasks.


The `/gemini` slash command demonstrates a useful pattern for integrating different models within a single workflow.

Facilitates seamless switching between models for different tasks, maximizing efficiency and leveraging the strengths of each model.

Develop similar integration mechanisms for other models in our multi-agent systems, allowing agents to dynamically select the best model for a given task.


Industry validation from companies like Cognition, JetBrains, and Resemble AI highlights the real-world applicability of Gemini 3 Flash in agentic coding and other tasks.

Provides evidence of the model's effectiveness and can be used to build confidence in potential clients.

Use these case studies in marketing materials and sales pitches to demonstrate the value of Gemini 3 Flash and our multi-agent systems.


Flash's 1M token context window allows for processing large codebases and documents, but its long context reliability is lower than Pro's.

Requires careful management of context size and potential use of techniques like summarization or retrieval-augmented generation to maintain accuracy.

Implement strategies for managing long context in our multi-agent systems, such as breaking down large tasks into smaller subtasks or using a knowledge graph to store and retrieve relevant information.


Flash's hallucination rate (91% accuracy) necessitates robust output validation mechanisms.

Highlights the need for automated testing, human-in-the-loop review, and other validation techniques to ensure the reliability of agent outputs.

Develop and implement comprehensive validation pipelines for agent outputs, including automated testing, human-in-the-loop review, and anomaly detection.


The Genesis KG already contains relevant information on multi-agent orchestration from IndyDevDan, which should be leveraged.

Provides a starting point for understanding and implementing multi-agent orchestration patterns.

Review and integrate the existing KG entities and axioms related to multi-agent orchestration into our current systems.


IndyDevDan's GitHub repositories (Hooks Mastery, Observability) offer valuable insights into building and monitoring agentic systems.

Provides practical examples and tools for developing and deploying agentic systems.

Explore IndyDevDan's GitHub repositories for inspiration and potential code reuse in our multi-agent systems.


The media_resolution parameter can be used to save tokens when processing images and videos.

Reduces the cost of multimodal tasks by optimizing the resolution of input media.

Implement a mechanism for dynamically adjusting the media_resolution parameter based on the task requirements and budget constraints.


Skills are automatically discovered and invoked based on user intent, unlike slash commands.

This highlights the importance of designing skills that are easily discoverable and match user intent for a seamless user experience.

Prioritize intent matching and natural language understanding in skill design to enable automatic discovery and invocation.


Progressive disclosure architecture is crucial for managing context bloat in skills.

This prevents overwhelming the agent with unnecessary information, improving performance and reducing costs.

Implement progressive disclosure in all skill designs, using SKILL.md as the central hub and loading additional files only when needed.


The 'Core Four' (Context, Model, Prompt, Tools) are the fundamental building blocks of agentic capabilities.

Provides a clear framework for designing and building agent skills, ensuring a consistent and effective approach.

Emphasize the Core Four in training materials and development processes for AI voice agents.


Forking allows for parallel execution of tasks by spawning new terminal windows with summarized context.

This enables significant performance improvements and scalability for multi-agent systems.

Integrate forking capabilities into the multi-agent orchestration system to enable parallel task execution.


Context forking preserves continuity by using YAML summary templates to hand off conversation state between agents.

Ensures seamless transitions between agents and maintains context across different tasks.

Develop robust context forking mechanisms using YAML templates to preserve conversation history and relevant information.


SKILL.md routing logic enables condition-based execution paths within skills.

This allows for flexible and adaptable skills that can handle different scenarios and user requests.

Implement routing logic in SKILL.md files to enable dynamic skill behavior based on user input and context.


The cookbook pattern separates instruction files per execution path, keeping context minimal.

Improved context management and efficiency for complex skills with multiple execution paths.

Adopt the cookbook pattern for complex skills to improve context management and reduce processing overhead.


VS Code snippets can significantly accelerate agentic engineering workflows.

Reduces development time and increases productivity for building and maintaining agent skills.

Create and distribute VS Code snippets tailored to the specific needs of the AI agency's development team.


Global skill installation makes skills available across all projects.

Promotes code reuse and consistency across different projects.

Establish a system for managing and deploying skills globally to ensure consistency and reuse across all projects.


Safety patterns, such as running `--help` before execution and avoiding modifying template files, are crucial for agent security.

Mitigates risks associated with agent execution and protects against unintended consequences.

Implement and enforce safety patterns in all agent skills to ensure secure and reliable operation.


The Fork Terminal Skill enables spawning Claude Code, Gemini CLI, or raw CLI commands from within a running session.

This allows for dynamic tool selection and execution, enhancing agent capabilities.

Explore and potentially adapt the Fork Terminal Skill for integration into the multi-agent orchestration system.


The video highlights the importance of defining the goal, purpose, deliverables, and file structure BEFORE coding.

Ensures a structured and efficient development process, leading to better outcomes.

Implement a rigorous planning phase for all skill development projects, emphasizing clear goal definition and structured design.


Skills are modular, context-aware capabilities stored in `.claude/skills/` directories.

This modularity allows for easy management, updates, and reuse of agent capabilities.

Adopt a modular skill architecture, organizing skills into dedicated directories for easy management and deployment.


Focus on building custom, domain-specific agents for production systems to achieve top 1% engineering output.

My TOP 5 Agentic Bets: Multi-Agent UI & Compute Scaling (Not for beginners)

Generic agents are insufficient for achieving significant competitive advantage.

Prioritize development of custom agent creation tools and domain-specific agent templates.


Engineers should focus on teaching agents rather than performing tasks themselves, deprecating old skills accordingly.

This paradigm shift is crucial for maximizing productivity and future-proofing engineering careers.

Develop training programs and documentation to help engineers transition to an agent-teaching role.


Address the planning and review constraints in agentic coding to unlock significant performance improvements.

Overcoming these constraints can lead to 1-2 orders of magnitude increase in output.

Invest in research and development of improved planning and review mechanisms for agentic workflows.


Explore multi-agent UIs, especially voice interfaces, to increase the information rate between users and agents.

Chat interfaces are insufficient; voice offers a more efficient communication channel.

Develop voice-enabled interfaces for agent interaction, focusing on use-case-specific applications.


Maximize compute usage by running agents 24/7 to solve problems autonomously.

This enables living software that works while you sleep, leading to true agentic automation.

Design infrastructure and architecture to support always-on agents, optimizing for cost and efficiency.


Prepare for a potential compute crunch by diversifying compute sources and stockpiling cheap compute credits.

Limited compute supply could hinder agentic development and deployment.

Establish partnerships with multiple compute providers and explore cost-effective compute options.


Build proactive agents that prompt users with opportunities and next steps, rather than just responding to commands.

This two-way prompt system can significantly enhance user engagement and agent effectiveness.

Implement mechanisms for agents to proactively analyze data and suggest actions to users.


Develop Agentic Developer Workflows (ADWs) - pipelines combining traditional engineering with agent orchestration.

ADWs are the most powerful abstraction for engineers right now.

Design and implement ADWs for common software development tasks.


Focus on use-case-specific multi-agent systems, rather than generic tools.

Generic tools are less effective than tailored solutions.

Prioritize development of multi-agent systems for specific business needs and industries.


The engineer is the bottleneck, not the models, tools or agents.

Focus on empowering engineers to effectively leverage AI.

Provide training and resources to help engineers become proficient in agentic engineering.


Offer a course or training program similar to Tactical Agentic Coding (TAC) to teach engineers how to build and deploy custom agents.

This can generate revenue and establish the agency as a thought leader.

Develop and market a comprehensive agentic coding course.


Explore revenue generation through offering compute resources optimized for agentic workloads.

Capitalize on the predicted compute crunch and increasing demand for agentic compute.

Develop a compute offering tailored to the needs of agentic applications.


Skill atrophy from agent delegation is guaranteed, not potential -- embrace it.

Resisting this change hinders progress and innovation.

Encourage and support engineers in focusing on higher-level tasks and strategic thinking.


Build a 24/7 always-on agent that monitors, generates, and ships autonomously.

Demonstrates the power of autonomous agents and provides a valuable service.

Develop and deploy a 24/7 autonomous agent for a specific business function.


Consider offering voice-first command center solutions to businesses, leveraging multi-agent UIs.

Taps into the trend of more efficient agent interaction.

Develop a voice-controlled multi-agent UI for business applications.


Model intelligence is becoming less important than the agentic system built around it.

I Gave Gemini 3 Pro Its Own Computer... It's Official, Claude Code Has COMPETITION

Focus on building robust agentic systems rather than solely chasing the latest model.

Prioritize development of agent skills, workflows, and orchestration over chasing the latest LLM benchmarks.


Giving agents dedicated cloud computers (sandboxes) enables massive parallel compute and complete isolation.

Allows for safer and more scalable agent deployments, enabling complex tasks and experimentation.

Evaluate and integrate E2B or a similar agent sandbox provider into the AI agency's infrastructure.


The 'Best of N' pattern involves running multiple agents on the same task and selecting the best result.

Improves the reliability and quality of agent outputs by leveraging the strengths of different models and mitigating individual agent failures.

Implement a 'Best of N' workflow for critical tasks, allowing for comparison and selection of the optimal output.


Agent skills can be shared across different models by using a shared memory file for reprogramming.

Enables model-agnostic agentic workflows, allowing the agency to leverage the best skills regardless of the underlying model.

Design agent skills to be model-agnostic and implement a shared memory file approach for cross-agent skill sharing.


E2B sandboxes can host results with public URLs, enabling easy sharing and demonstration of agent outputs.

Facilitates showcasing agent capabilities to clients and prospects, potentially accelerating sales.

Integrate sandbox result hosting into the agent workflow to automatically generate and share demo URLs.


The most relevant benchmark is how agents perform against YOUR specific use case.

Avoids over-reliance on general benchmarks and focuses on practical performance for client needs.

Develop internal benchmarks tailored to the agency's specific use cases and client requirements.


Claude Code demonstrated reliable task completion, even compared to Gemini 3 Pro with superior benchmarks.

Highlights the importance of agentic systems and tooling over raw model intelligence for reliable task execution.

Investigate Claude Code's agentic system for best practices in reliability and task completion.


The agent sandbox paradigm provides more autonomy, less management, and better security for agents.

Reduces operational overhead and risks associated with running agents in local environments.

Transition from local worktrees to cloud-based agent sandboxes for improved security and scalability.


Reprogramming agents with backslash commands allows for custom workflows and skill execution.

Provides a flexible and extensible mechanism for controlling agent behavior and integrating custom skills.

Implement a backslash command system for agent reprogramming, enabling custom workflows and skill execution.


E2B sandboxes auto-expire, requiring lifecycle management for creation, usage, result copying, and destruction.

Prevents resource wastage and ensures efficient utilization of cloud resources.

Implement a lifecycle management system for agent sandboxes, including automatic creation, usage, result copying, and destruction.


Sandboxes enable full-stack application building by agents, including front-end, back-end, and database.

Expands the scope of agent capabilities to include complex application development.

Develop agent skills for building full-stack applications, leveraging the capabilities of sandboxes.


Sandbox-based testing provides full isolation, parallel execution, and prevents local pollution.

Improves the reliability and efficiency of testing processes.

Implement sandbox-based testing for agent skills and applications.


The video highlights the importance of scaling compute to scale impact.

Reinforces the need for a scalable infrastructure to support the agency's growing AI agent operations.

Continuously evaluate and optimize the agency's compute infrastructure to support increasing agent workloads.


The presenter uses a CLAUDE.md file to store reprogramming instructions for agents.

Provides a simple and effective way to configure agent behavior and memory.

Standardize the use of CLAUDE.md or similar memory files for agent configuration and behavior management.


The presenter reprogrammed Gemini CLI to use Claude Agent Skills.

Demonstrates the potential for cross-model skill sharing and interoperability.

Explore the possibility of enabling Gemini CLI agents to access and utilize existing Genesis skills.


Implement Gemini Computer Use as a validation tool for agents to visually verify UI work.

BIG 3 SUPER AGENT: Gemini 2.5 Computer Use, OpenAI Realtime API, Claude Code

Improves reliability and accuracy of agent outputs by adding visual verification, reducing reliance on self-reporting.

Integrate Gemini 2.5 Computer Use API into Genesis agents to enable visual validation of UI changes and application functionality.


Adopt a thin orchestration layer pattern where the orchestrator only handles CRUD operations against agents, remaining model/tool agnostic.

Increases flexibility and adaptability of the multi-agent system, allowing for easy integration of new models and tools without requiring significant code changes.

Refactor the Genesis hive coordinator to focus solely on CRUD operations for agents, decoupling it from specific model or tool implementations.


Develop voice orchestration capabilities using Telnyx and the OpenAI Realtime API to enable voice-controlled agent management.

Provides a more intuitive and efficient way to interact with and control the multi-agent system, enabling hands-free operation and faster task execution.

Integrate the Telnyx voice bridge with the OpenAI Realtime API to create a voice-controlled interface for creating, commanding, and monitoring Genesis agents.


Enforce explicit agent boundaries by defining file/directory ownership for each agent to prevent conflicts when spawning parallel agents.

Reduces the risk of agents interfering with each other's work, leading to more reliable and predictable system behavior.

Implement a mechanism to explicitly define file and directory ownership for each agent during task assignment to prevent unintended modifications and conflicts.


Create a command-line interface (CLI) that accepts prompts and dispatches them to the multi-agent system, mirroring the functionality of the voice interface.

Offers an alternative text-based interface for interacting with the multi-agent system, providing flexibility and accessibility for users who prefer or require text-based input.

Develop a CLI that accepts prompts as input and routes them to the appropriate agents within the Genesis system for processing and execution.


Study the Big 3 Super Agent repository to understand orchestrator tool definitions and agent management patterns.

Provides valuable insights into the practical implementation of multi-agent systems and orchestration strategies, informing the development and improvement of Genesis.

Clone and analyze the Big 3 Super Agent repository to identify best practices and reusable components for agent orchestration and management.


Leverage agent output logging for crash recovery by re-listing agents and re-reading their output files to restore system state.

Ensures system resilience and minimizes data loss in the event of crashes, enabling quick recovery and continued operation.

Enhance the existing `hive/progress/` handoff pattern and agent output logging to facilitate seamless crash recovery by re-listing agents and re-reading their output files.


Support both text and voice interfaces for the same orchestration system to provide flexibility in how users interact with the agents.

Increases accessibility and user convenience by offering multiple input modalities for controlling and managing the multi-agent system.

Ensure that the Genesis command center supports both terminal text input (current) and voice input via Telnyx (planned) for a unified orchestration experience.


Implement custom slash commands for reusable agent workflows to streamline common tasks and improve efficiency.

Reduces the need for repetitive manual commands and enables the creation of standardized, easily accessible agent workflows.

Expand the existing set of 64 skills to include custom slash commands that can be invoked by both humans and other agents to automate common tasks and workflows.


Focus on building the system that builds the system, aiming for a meta-level orchestration system that can produce applications.

Enables the creation of a self-improving and evolving multi-agent system that can autonomously generate and maintain applications.

Prioritize the development of meta-level orchestration capabilities that allow the Genesis system to automatically generate, deploy, and maintain applications based on predefined specifications and goals.


Explore offering specialized agents with specific capabilities as a premium service.

Creates a new revenue stream by providing customized agent solutions tailored to specific business needs.

Identify high-demand agent capabilities and develop specialized agents that can be offered as premium services to clients.


Offer multi-agent orchestration systems as a service, allowing businesses to leverage the power of composable AI without needing to build their own infrastructure.

Generates revenue by providing access to a powerful and flexible AI platform that can be adapted to a wide range of business applications.

Package the Genesis multi-agent orchestration system as a service that can be offered to businesses on a subscription basis, providing access to a composable AI platform without the need for in-house development.


Emphasize the composability of the AI agency's offerings, highlighting the ability to combine different models and tools to create customized solutions.

Differentiates the agency from competitors who focus on single-provider solutions, attracting clients who value flexibility and customization.

Develop marketing materials that highlight the composability of the Genesis platform, emphasizing the ability to combine different models and tools to create tailored solutions for specific business needs.


Implement real-time monitoring of agent activities using hooks to provide insights into agent performance and identify potential issues.

Enables proactive identification and resolution of issues, improving the reliability and performance of the multi-agent system.

Leverage the existing 6-hook observability system (PostToolUse, SubagentStart/Stop, etc.) to provide real-time monitoring of agent activities and identify potential issues.


Use cheap/fast model summaries for agent activity monitoring to efficiently identify errors and prioritize deeper investigation.

Reduces the computational cost of monitoring agent activities and allows for efficient identification of potential issues.

Implement a mechanism to generate cheap/fast model summaries of agent activities, allowing for efficient identification of errors and prioritization of deeper investigation.


Create separate, dedicated email accounts for each AI agent to limit data leakage and accidental access to sensitive information.

DO THIS to STOP Claude CoWork & OpenClaw From Leaking Your Data

Prevents AI agents from accessing personal or confidential data, reducing privacy risks and potential data breaches.

Implement a system for creating and managing dedicated email accounts for each AI agent, ensuring proper data segregation.


Implement a mechanism for AI agents to always CC the user on emails, clearly indicating that the email is being sent by a bot.

Maintains transparency and prevents AI agents from impersonating users, building trust and avoiding miscommunication.

Develop a skill or setting within the multi-agent orchestration system to automatically CC users on all AI-generated emails.


Utilize decentralized VPNs with rotating IPs to avoid AI platform restrictions and region blocking.

Ensures uninterrupted access to AI tools and features, regardless of location or platform restrictions.

Integrate decentralized VPN services into the AI agency's infrastructure to provide reliable and unrestricted access to AI platforms.


Regularly rotate API keys and MCP access for all AI tools to prevent unauthorized access and potential financial losses due to API scams.

Reduces the risk of API key leakage and unauthorized usage, protecting against financial losses and data breaches.

Implement a scheduled API key rotation system with reminders and automated procedures for all AI tools used by the agency.


Implement a process to disable data sharing and training features in AI tools to prevent them from learning from sensitive user data.

Protects user privacy and prevents AI models from being trained on confidential information, mitigating potential data leaks.

Develop a guide and checklist for disabling data sharing and training features in popular AI tools, ensuring compliance with privacy regulations.


Use AI-powered redaction tools to remove sensitive information from documents before uploading them to AI platforms.

Prevents AI models from accessing and potentially leaking sensitive information contained in uploaded documents.

Evaluate and integrate AI redaction tools into the agency's workflow to automatically remove sensitive data from documents before they are processed by AI agents.


The market is concerned about data privacy when using AI tools.

Highlighting privacy features can be a competitive differentiator.

Emphasize data privacy and security measures in marketing materials and product offerings.


Offer a service to audit and secure client's AI workflows, including API key rotation, data redaction, and VPN setup.

Creates a new revenue stream by addressing a critical client need for AI security and compliance.

Develop a packaged service offering focused on AI security and compliance, including regular audits and remediation steps.


Users are actively searching for solutions to prevent data leakage from AI tools.

Indicates a strong demand for AI security solutions, creating a market opportunity.

Conduct keyword research to identify relevant search terms and optimize marketing content to attract users seeking AI security solutions.


OpenClaw has a history of leaking API keys.

Highlights the importance of robust security measures and regular API key rotation.

Prioritize security audits and API key management for AI agents that interact with OpenClaw or similar platforms.


The video recommends URnetwork decentralized VPN with a lifetime deal on AppSumo.

Provides a specific tool recommendation for bypassing region restrictions and improving AI tool access.

Evaluate URnetwork and other decentralized VPN solutions for potential integration into the AI agency's infrastructure.


Reviews are a high-need, low-support service that businesses will pay for monthly, making them an ideal entry point for upselling higher-ticket AI services.

How I Make $44,600/Month Helping Businesses Get REVIEWS!

Provides a stable revenue base with minimal support overhead, freeing up resources for more complex AI solutions.

Prioritize building an AI-powered review management solution as a core offering.


Leverage Facebook groups, particularly industry-specific and software-specific groups, to establish authority and generate leads for review management services.

Offers a cost-effective channel for acquiring customers and building a strong reputation within a niche.

Dedicate resources to identifying relevant Facebook groups, engaging with members, and offering value-added content.


Personalized images in review request messages significantly increase conversion rates.

Demonstrates the power of personalized communication and its impact on customer engagement.

Integrate image personalization capabilities into the review management system, potentially using AI to generate personalized visuals.


Automated review response using AI is a key feature for maintaining client satisfaction and reducing support burden.

Reduces manual effort for businesses and ensures timely responses to customer feedback.

Develop or integrate an AI-powered review response system that can automatically generate appropriate responses based on review content and sentiment.


CRM integration is crucial for automating the review request process and ensuring consistent customer engagement.

Streamlines the review generation process and minimizes manual intervention.

Develop or integrate a CRM connector tool that automatically triggers review requests based on customer data and purchase history.


Review reactivation (reaching out to past customers) is a quick win that builds trust and opens doors for upselling.

Provides immediate value to clients and establishes credibility for future service offerings.

Incorporate a review reactivation campaign as a standard component of the onboarding process.


Position review management as a foundational service that complements and enhances other AI-powered marketing solutions like SEO and lead generation.

Creates a clear value proposition for clients and facilitates upselling to higher-ticket services.

Develop marketing materials that highlight the synergy between review management and other AI-driven marketing strategies.


Focus on backend automation to minimize client interaction and support requests.

Reduces operational costs and allows for scaling the business.

Prioritize the development of self-sufficient AI agents that can operate autonomously with minimal human intervention.


Upselling to automated SEO services after delivering results with review management is a successful revenue generation strategy.

Increases revenue per client and expands the service offerings.

Develop an automated SEO solution that can be seamlessly integrated with the review management system.


Building a community around the service (e.g., Facebook group) fosters loyalty and generates organic referrals.

Creates a valuable network for clients and reduces reliance on paid advertising.

Establish a community forum or group where clients can connect, share best practices, and provide feedback.


The success of ReviewHarvest relies heavily on GoHighLevel.

Highlights the importance of selecting the right platform for building and deploying AI agents.

Evaluate GoHighLevel and similar platforms for their suitability in building and deploying AI-powered review management solutions.


The speaker uses a simple automation to request, respond, and repurpose reviews.

Highlights the value of a streamlined workflow for review management.

Design the AI agent to handle all aspects of review management, from requesting to responding and repurposing.


The speaker highlights the importance of making a business's Google Business Profile an accurate reflection of how happy their customers are.

This emphasizes the importance of sentiment analysis and accurate representation of customer feedback.

Incorporate sentiment analysis into the AI agent to accurately assess customer feedback and tailor responses accordingly.


Google AI Studio's simplified billing and navigation lowers the barrier to entry for building and selling AI solutions to businesses.

Google AI Studio's New Features Are a Gold Mine (Here's Why)

Reduces friction for developers and allows faster iteration and deployment.

Prioritize ease of use and simplified onboarding in our AI agent platform to attract a wider range of users.


Google Search Grounding and Google Maps Grounding are now easily accessible, enabling the creation of AI agents that can access real-time information and location data.

Enables the creation of more useful and context-aware AI agents for businesses.

Integrate and highlight the use of real-time data sources and location-based services in our AI agent offerings.


Gemini 3 Flash offers faster performance without sacrificing quality, making it ideal for client-facing AI services.

Improved speed leads to better user experience and faster deal closures.

Benchmark and prioritize fast, high-quality models like Gemini 3 Flash in our AI agent platform.


Fine-tuning models with client-specific data allows for the creation of custom AI solutions that can be sold at a premium.

Increases the value proposition of AI agents and allows for higher pricing.

Develop a robust fine-tuning pipeline and offer custom AI agent training services to clients.


One-click deployment to Cloud Run simplifies the deployment process and reduces infrastructure costs.

Reduces the technical complexity and cost of deploying AI agents.

Streamline the deployment process for our AI agents, potentially leveraging serverless technologies like Cloud Run.


AI Studio's integration with Workspace tools (Gmail, Sheets, Docs) facilitates adoption by businesses.

Reduces the learning curve and increases the likelihood of adoption.

Prioritize integrations with popular business tools and workflows to make our AI agents more accessible.


Saved system instructions enable faster development and scaling of AI solutions.

Reduces development time and allows for greater efficiency.

Implement a system for saving and reusing prompts and configurations across different AI agent projects.


The speaker suggests focusing on specific local industries (e.g., home services) to build and sell AI solutions.

Allows for targeted marketing and efficient solution development.

Identify and target specific industries with tailored AI agent solutions.


Content generation tools for agencies (social posts, blog ideas, email copy) are a viable revenue stream.

Addresses a clear market need and allows for recurring revenue.

Develop AI agents specifically designed for content creation and offer them as a subscription service to marketing agencies.


Real estate tools (property research assistant) are a potential revenue opportunity.

Addresses a specific need in the real estate industry.

Develop AI agents that can assist real estate agents with property research and market analysis.


Improved collaboration features in AI Studio facilitate teamwork and faster development.

Enhances team productivity and reduces development time.

Implement collaboration features in our AI agent platform to enable teamwork and knowledge sharing.


The video highlights the importance of understanding business needs and connecting the dots with AI tools.

Emphasizes the need for a business-focused approach to AI development.

Train our team to understand business needs and translate them into AI solutions.


The speaker emphasizes the time-sensitive opportunity to capitalize on these new features before widespread adoption.

Highlights the importance of acting quickly to gain a competitive advantage.

Accelerate our development and marketing efforts to capitalize on the current window of opportunity.


MCP servers consume significant context tokens, reducing agent effectiveness.

Why are top engineers DITCHING MCP Servers? (3 PROVEN Solutions)

Reduced agent performance and increased cost due to token usage.

Audit current MCP server usage and measure token consumption. Prioritize migration to CLI-first or script-based tools where appropriate.


Progressive disclosure of tool definitions can save up to 90% of context tokens.

Significantly improved agent performance and reduced token costs.

Implement progressive disclosure for tool definitions in the multi-agent orchestration system.


CLI-first development pattern allows for human and agent interoperability.

Increased development efficiency and flexibility in tool usage.

Adopt a CLI-first development approach for all new AI voice agent tools and multi-agent system components.


Prime prompts are crucial for teaching agents how to use CLI tools effectively.

Improved agent performance and reduced errors when using CLI tools.

Develop standardized prime prompt templates for each CLI tool used by AI voice agents and multi-agent systems.


Single-file scripts with declared dependencies offer high portability and context preservation.

Improved agent efficiency and ease of deployment.

Explore using Astral UV for Python single-file scripts with dependencies declared at the top for specific agent tasks.


Focused, single-purpose agents can sidestep many context engineering problems.

Simplified agent design and reduced context management overhead.

Design AI voice agents and multi-agent systems with a focus on specific tasks to minimize context requirements.


Code duplication can be acceptable for agent efficiency.

Improved agent performance by reducing context clutter.

Consider code duplication in isolated scripts to minimize context and improve agent performance, even if it increases code maintenance overhead.


Prediction markets can be used as information sources for agents.

Provides agents with real-time sentiment and predictive data.

Explore integrating prediction market data into AI voice agents to provide insights into future events and market sentiment.


Prompt engineering is critical for agent performance, even before context engineering.

Invest in prompt engineering expertise and develop best practices for crafting effective prompts for AI voice agents and multi-agent systems.


The video provides a practical demo using a Kalshi prediction markets API.

Offers a concrete example of how to apply the discussed techniques.

Study the Kalshi API example to understand how to implement CLI-first and progressive disclosure patterns in real-world applications.


The 80/10/10 rule provides a guideline for choosing the right approach based on tool ownership and context needs.

Helps prioritize development efforts and optimize resource allocation.

Use the 80/10/10 rule as a framework for deciding when to use MCP servers, CLI tools, scripts, or skills in AI voice agent and multi-agent system development.


Context bleeding from MCP servers can lead to quantifiable efficiency losses.

Reduced agent performance and increased operational costs.

Establish a system for tracking and quantifying context token usage in AI voice agents and multi-agent systems to identify and address context bleeding issues.


The video highlights the importance of controlling what an agent sees.

Improved agent accuracy and reduced errors.

Implement mechanisms to carefully control the information provided to AI voice agents and multi-agent systems to ensure they have access to the necessary context without being overwhelmed by irrelevant data.


The video emphasizes the importance of portability in agentic engineering.

Increased flexibility and reduced vendor lock-in.

Prioritize portability when developing AI voice agents and multi-agent systems to ensure they can be easily deployed and adapted to different environments and platforms.


Prioritize custom agent development using Claude Code SDK for each Genesis product (ReceptionistAI, Sunaiva, AIVA) to leverage domain-specific system prompts and tailored toolsets.

Agentic Coding ENDGAME: Build your Claude Code SDK Custom Agents

Significantly improves agent performance and efficiency by focusing compute on specific domain problems.

Immediately begin prototyping custom agents for each product, starting with defining system prompts and tool requirements.


Implement model tiering for agents based on complexity: Haiku/Kimi for simple routing, Sonnet for coordination, Opus for reasoning, Gemini Flash for high-volume extraction.

Optimizes cost and performance by matching model capabilities to agent needs.

Review and adjust model assignments for existing agents, prioritizing high-volume or computationally intensive tasks.


Strip unnecessary default tools from Claude Code agents to reduce context window consumption and improve focus.

Maximizes the context available for domain-specific knowledge, leading to more accurate and relevant responses.

Audit existing agent configurations using `/context` to identify and remove unused tools.


Utilize the `create_sdk_mcp_server()` function for in-memory MCP server creation to provide custom tools to agents without the overhead of external servers.

Simplifies deployment and reduces latency for agents requiring custom tools, especially for Thin Coordinator worker patterns.

Integrate `create_sdk_mcp_server()` into the tool management system and migrate relevant agents to use in-memory tools.


Explore the Micro SDLC pattern (Plan->Build->Review->Ship) for Genesis sprints, assigning each stage to a dedicated agent with WebSocket progress reporting.

Automates and streamlines the software development lifecycle, improving efficiency and visibility.

Pilot the Micro SDLC pattern with a small team, focusing on integrating agents for each stage and implementing WebSocket communication.


Treat the system prompt as the core definition of each Genesis product, storing it in a dedicated file and carefully crafting it to define the agent's personality, capabilities, and constraints.

Ensures consistency and clarity in agent behavior, making it easier to manage and improve agent performance.

Create dedicated system prompt files for each product and establish a process for reviewing and updating them.


Implement context window auditing using `/context` on all active Genesis agents to monitor token consumption and identify opportunities for optimization.

Reduces costs and improves agent performance by ensuring efficient use of the context window.

Schedule regular context window audits and establish a process for addressing identified issues.


Leverage the `ClaudeSdkClient` class for continuous conversations with agents, enabling more complex and interactive workflows.

Allows for more sophisticated agent interactions and supports use cases requiring follow-up questions and context retention.

Explore the use of `ClaudeSdkClient` in existing agents and identify opportunities to implement continuous conversation workflows.


Explore the Peter Framework (in-loop -> out-of-loop -> ZTE) for review processes to move towards Zero Touch Engineering.

Increases efficiency and reduces manual intervention in the development process.

Research and design a plan for implementing the Peter Framework in relevant workflows.


Investigate the related IndyDevDan videos and GitHub repository for additional insights and code examples.

Provides access to valuable resources and accelerates the development of custom agents.

Assign a team member to review the videos and repository and identify relevant code patterns and techniques.


Develop a pricing strategy that reflects the tiered model usage (Haiku/Kimi, Sonnet, Opus, Gemini Flash), charging more for agents that require more powerful models.

Ensures profitability by aligning pricing with the computational resources consumed by each agent.

Analyze current pricing models and adjust them to reflect the cost of different model tiers.


Offer custom agent development as a premium service, leveraging the expertise gained from building domain-specific agents for Genesis products.

Creates a new revenue stream and positions Genesis as a leader in AI agent development.

Develop a marketing strategy for custom agent development services and identify potential clients.


Track and analyze agent performance metrics (e.g., token consumption, response time, accuracy) to identify areas for improvement and optimize resource allocation.

Enables data-driven decision-making and continuous improvement of agent performance.

Implement a system for tracking and analyzing agent performance metrics.


Monitor the competitive landscape for advancements in custom agent development and multi-agent orchestration to stay ahead of the curve.

Ensures that Genesis remains competitive and continues to offer cutting-edge AI solutions.

Assign a team member to monitor the competitive landscape and report on relevant developments.


Git Worktree Isolation is now built into Claude Code, enabling conflict-free parallel agent execution on the same repository.

Claude Code New Updates: Better Desktop App, Simple Mode, In-built Worktrees, New Hooks & MORE!

Enables more complex multi-agent workflows and reduces development friction.

Integrate Git Worktree Isolation into our multi-agent orchestration system to allow agents to work independently on the same codebase.


Claude Code Desktop includes live app preview, code review panel, and GitHub integration (automated PR fixes and merging).

Provides a more integrated and efficient development workflow.

Explore integrating similar features into our AI voice agent development environment to streamline testing and deployment.


Claude Opus 4.6 has a 200k context window (1M in beta) and improved long task completion (14 hours at 50% success rate).

Enables handling larger and more complex projects without losing context.

Evaluate Opus 4.6 for our AI voice agents and multi-agent systems to improve performance on complex tasks.


Claude Code now has automemory, automatically recording and recalling learnings across sessions.

Improves agent learning and adaptation over time, reducing repetitive tasks.

Implement a similar automemory feature in our AI voice agents and multi-agent systems to enhance their learning capabilities.


Claude Code Security offers automated security analysis using semantic reasoning, catching vulnerabilities that pattern-matching tools miss.

Provides a more robust security analysis compared to traditional methods.

Investigate incorporating semantic reasoning-based security analysis into our AI voice agent development process.