Code Evolution

Architecture

orchestrator (you)
├── spawn agents (Task tool, subagent_type='general-purpose')
├── evaluate solutions (run evaluate.py)
├── manage archive (best solutions per generation)
└── plan next generation

Critical Principle: Agent Autonomy

NEVER write solution code yourself. You (the orchestrator) ONLY:

• Create the fixed evaluation harness (read-only for agents)
• Spawn autonomous subagents via Task tool
• Evaluate results using the harness
• Plan next generation based on results

Agents have full autonomy to implement their assigned approach. You don't guide their code - you guide their problem-solving strategy.

Workflow

Phase 0: Setup (Orchestrator Only)

Create the immutable harness - agents can ONLY use, never alter:

1. problems/<name>/problem.md - problem definition (READ-ONLY for agents)
2. problems/<name>/evaluation/evaluate.py - evaluation function (FROZEN, not modifiable by agents)
3. problems/<name>/config.json - benchmark, constraints, metadata

Agents receive paths to these files but cannot modify them.

Phase 1: Generation Loop (3-7 generations)

1. Plan Strategies: Design 2-4 different approaches for agents to explore
2. Spawn Agents: Use Task tool with subagent_type='general-purpose' (15s timeout per agent)
   • Each agent gets problem description, their specific approach, and path to evaluator
   • Agents write solutions to generations/gen{N}/agent_{id}.py
   • Agents run themselves: subprocess.run([sys.executable, agent_file])
   • Output: JSON with "score" and "circles"
3. Evaluate: You run evaluator on agent outputs (agents cannot run this)
4. Cross-Inspiration: Share winning ideas with next generation agents for inspiration
5. Prune: Keep only the best 1-2 approaches from previous generation
6. Archive: Store best solution to generations/archive/

Phase 2: Cross-Inspiration & Pruning

Between generations:

• Reference winners: Show agents the best previous solution's strategy
• Prune dead approaches: Stop testing approaches that underperform
• Mix winning ideas: Combine best techniques from multiple agents
• Diversify within winners: Vary parameters (seeds, iteration counts, thresholds)

File Structure

problems/<name>/
├── problem.md
├── config.json
├── evaluation/evaluate.py
└── generations/
    ├── gen1/agent_*.py
    └── archive/best_solution.py

Core Design Principles

Separation of Concerns

• Orchestrator role: Strategy planning, harness building, result evaluation, pruning
• Agent role: Implementation autonomy within their assigned strategy
• Harness: Frozen, read-only, immutable contract between them

Evolution Mechanics

1. Diverse exploration (Gen 1-3): Different approaches find different optima
2. Cross-inspiration (Gen 2+): Winning ideas inspire next generation
3. Pruning (Gen 3+): Kill weak approaches, double down on winners
4. Multi-start within winners: Vary parameters of proven strategies (+2-5% improvement)
5. Validation first: Invalid solutions score 0 - harness is source of truth

Evolution Strategy

Phase	Generations	Orchestrator Action
Explore	1-3	Spawn 3-4 agents with diverse strategies. Find winners.
Prune	After Gen 2-3	Kill underperforming approaches. Keep 1-2 best.
Cross-Inspire	Before Gen 4+	Share winning solution code/strategy with next agents.
Exploit	4-5	Spawn agents that refine/combine winning approaches. Vary seeds/params.
Polish	6-7	Multi-start within best approach. Push toward benchmark.

Orchestrator Responsibilities

What YOU Do (Never Delegate)

• Create immutable evaluation harness (problem definition, evaluator, config)
• Spawn agents with Task tool
• Analyze results and plan next generation
• Prune: Decide which approaches to continue, which to kill
• Cross-inspire: Extract winning ideas and share with next agents
• Archive best solutions

What Agents Do (Full Autonomy)

• Implement their assigned strategy
• Write solution code
• Self-validate before output
• Run themselves and produce JSON output

Cross-Inspiration Strategy

After each generation, extract and communicate:

## What Worked
- Agent X achieved Y% with [strategy description]
- Key insight: [what made it work]
- Code reference: [location or snippet]

## What Failed
- Agent Z's [strategy] only achieved W%
- Likely issue: [root cause analysis]
- Don't repeat: [specific thing to avoid]

## Recommended Evolution
- Agents should build on: [winning strategy]
- Vary these parameters: [list of what to try]
- Combine techniques: [which ideas from multiple winners]

Agents use this to:

• Understand what works (cross-inspiration)
• Avoid dead ends (prune knowledge)
• Focus effort on proven directions

References

• Agent spawning: See references/agent-prompts.md
• Evaluator template: See references/evaluator-template.md

Adding New Problems

1. Create problems/<name>/problem.md (objective, constraints, benchmark, format)
2. Create problems/<name>/config.json (benchmark value, metadata)
3. Create problems/<name>/evaluation/evaluate.py (validate, score, evaluate functions)