# RWL Scaling & Dashboard Research PRD
*Deep Research: Scaling Ralph Wiggum Loops to AlphaEvolve Level - January 2026*

**Mission:** Research how to scale RWLs massively, build real-time dashboards, and achieve Google AlphaEvolve-level autonomous development in 1 week.

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### US-001: Real-Time Task Dashboard Systems
**Goal:** Research best real-time task execution dashboards for AI agent orchestration

**Acceptance Criteria:**
- Identify top 5 real-time task monitoring dashboards
- Document WebSocket/SSE streaming approaches
- Analyze progress visualization patterns
- Note open-source dashboard solutions
- Extract design patterns for RWL monitoring

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/dashboard_systems.md

**Technical Hints:**
Research Prefect, Dagster, Apache Airflow UIs. Look at n8n execution viewer. Analyze Temporal.io workflow visualization. Note real-time progress bars, status indicators, streaming logs.

**Dependencies:** None

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### US-002: Google AlphaEvolve Analysis
**Goal:** Deep dive on Google AlphaEvolve - autonomous code evolution system

**Acceptance Criteria:**
- Document AlphaEvolve architecture and approach
- Analyze their evolutionary programming methodology
- Note how they achieve autonomous code generation
- Extract scaling patterns for parallel execution
- Compare to Genesis RWL approach

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/alphaevolve_analysis.md

**Technical Hints:**
Research Google DeepMind AlphaEvolve announcements 2024-2025. Focus on evolutionary algorithms for code, fitness functions, population diversity. Note GPU/TPU scaling approaches.

**Dependencies:** None

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### US-003: Agentic Loop Orchestration Platforms
**Goal:** Research platforms that orchestrate thousands of concurrent AI agent tasks

**Acceptance Criteria:**
- Identify top agentic orchestration platforms 2025-2026
- Document their scaling architectures
- Analyze queue management systems
- Note cost optimization strategies
- Extract patterns for Genesis scaling

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/orchestration_platforms.md

**Technical Hints:**
Research Modal.com serverless, Ray distributed computing, Dask, Celery. Look at LangGraph multi-agent, CrewAI orchestration. Note Kubernetes-based agent scaling. Analyze token/cost management at scale.

**Dependencies:** None

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### US-004: Parallel Agent Execution Patterns
**Goal:** Research patterns for running thousands of AI agents in parallel

**Acceptance Criteria:**
- Document parallel execution architectures
- Analyze fan-out/fan-in patterns
- Note resource pooling strategies
- Extract context isolation techniques
- Identify bottlenecks at scale

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/parallel_patterns.md

**Technical Hints:**
Research OpenAI Swarm, Microsoft AutoGen, LangChain parallelization. Focus on worker pools, async execution, result aggregation. Note memory/context management across thousands of concurrent agents.

**Dependencies:** None

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### US-005: Cost Optimization at Scale
**Goal:** Research how to run thousands of RWLs cost-effectively

**Acceptance Criteria:**
- Calculate cost models for 1000+ daily RWLs
- Document model routing strategies (cheap first)
- Analyze caching and deduplication approaches
- Note batching and request optimization
- Extract budget management patterns

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/cost_optimization.md

**Technical Hints:**
Compare Gemini Flash ($0.01) vs Claude Sonnet ($0.15) vs local models ($0). Research semantic caching, prompt compression. Analyze tiered escalation economics. Calculate break-even for local GPU inference.

**Dependencies:** None

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### US-006: Real-Time Streaming Dashboard Tech
**Goal:** Research optimal tech stack for real-time RWL monitoring dashboard

**Acceptance Criteria:**
- Identify best WebSocket frameworks for streaming
- Document React/Vue dashboard patterns
- Analyze server-sent events vs WebSocket
- Note database options for execution logs
- Extract optimal frontend visualization libraries

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/dashboard_tech_stack.md

**Technical Hints:**
Research Socket.io, FastAPI WebSockets, Pusher. Look at Tremor, Recharts, Victory for visualizations. Note Supabase realtime, Redis pub/sub. Analyze Grafana/Prometheus for metrics.

**Dependencies:** None

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### US-007: Self-Improving Agent Systems
**Goal:** Research systems where agents improve their own execution over time

**Acceptance Criteria:**
- Document self-improving agent architectures
- Analyze learning-from-failure patterns
- Note automatic prompt optimization
- Extract evolutionary improvement mechanisms
- Compare to Genesis axiom generation

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/self_improving_agents.md

**Technical Hints:**
Research DSPy automatic prompt optimization, TextGrad, OPRO. Look at meta-learning for agents. Analyze reflexion and self-critique patterns. Note how to incorporate learnings across RWL runs.

**Dependencies:** None

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### US-008: Genesis AlphaEvolve Blueprint
**Goal:** Synthesize research into blueprint for Genesis to match AlphaEvolve in 1 week

**Acceptance Criteria:**
- Create 7-day sprint plan
- Define minimal viable scaling architecture
- List required infrastructure components
- Prioritize highest-impact features
- Output actionable implementation roadmap

**Files to Create:**
- /mnt/e/genesis-system/logs/rwl_scaling_research/ALPHAEVOLVE_BLUEPRINT.md

**Technical Hints:**
Combine all research learnings. Focus on: parallel RWL execution, real-time dashboard, cost optimization, self-improvement loops. Define daily milestones for 1-week sprint. Identify risks and mitigations.

**Dependencies:** US-001 through US-007
