Evolution vs Adaptiveness
Evolution is the mechanism. Adaptiveness is the result. Hive captures failure data and uses coding-agent driven changes to produce better next versions.Evolution Loop
- Execute: agent runs on real inputs
- Evaluate: outcomes are checked against criteria and constraints
- Diagnose: failure is localized to nodes, criteria, and decision traces
- Regenerate: graph/prompts/tools/edges are revised and redeployed
What Can Evolve
- Prompts and instructions
- Graph structure
- Edge conditions
- Tool selection
- Constraint and criteria tuning
Key Distinction
Evolution does not imply general intelligence gains. It increases reliability for observed failure patterns over generations.Why Logging Matters
Evolution quality depends on runtime evidence:- Node-level traces
- Decision logs
- Tool invocation outcomes
- Cost and latency metrics
Operational Guidance
- Treat every production failure as test input for the next iteration
- Capture reproducible failure context
- Add regression coverage before redeploying