> ## Documentation Index
> Fetch the complete documentation index at: https://docs.adenhq.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Evolution

> How Hive uses failure data to iteratively improve agent behavior

## 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

1. Execute: agent runs on real inputs
2. Evaluate: outcomes are checked against criteria and constraints
3. Diagnose: failure is localized to nodes, criteria, and decision traces
4. 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

Without this, changes are guesswork.

## Operational Guidance

* Treat every production failure as test input for the next iteration
* Capture reproducible failure context
* Add regression coverage before redeploying

> **Evolution in Practice**: Evolution is the engine of the iterate phase in Aden Hive's developer success cycle. Once your agent is deployed, [iteration strategies](/building/iteration) show you how to use production data to automatically improve agent behavior over time.

## Related Concepts

* [Goals and Outcomes](/building-agent/concepts/goals-outcomes)
* [Agent Graph](/building-agent/concepts/agent-graph)
* [Worker Agent](/building-agent/concepts/worker-agent)
* [Iteration](/building/iteration)