# Proactive Agent Pattern

A proactive agent observes its environment, identifies improvements, and
self-modifies within strict guardrails. This pattern is inspired by
OpenClaw's proactive agent skill.

## When to use

- Agents that run frequently and should improve over time
- Pipelines with measurable performance metrics
- Systems where the cost of not improving exceeds the risk of changes

## When NOT to use

- Simple pipelines that just need to run reliably
- Human-in-the-loop workflows (the human provides the feedback)
- New systems that haven't established a performance baseline yet

## Components

- **PROACTIVE-AGENT.md**: Agent template with proactive cycle, VFM protocol, self-healing
- **ADL-RULES.md**: Anti-Drift Limits — constraints that prevent uncontrolled drift
- **VFM-SCORING.md**: Value-First Modification — scoring rubric for proposed changes

## How ADL and VFM work together

ADL defines what the agent CANNOT do (hard boundaries).
VFM determines what the agent SHOULD do (prioritization within boundaries).

```
Proposed change
  → Check ADL constraints → BLOCKED if constraint violated
  → Score with VFM → IMPLEMENT if > 50, DEFER if <= 50
  → Log decision either way
```

## Integration with feedback loops

The proactive agent reads from:
- `feedback/FEEDBACK.md` — pipeline run outcomes
- `budget/cost-events.jsonl` — cost data
- `logs/audit.log` — tool call history
- `memory/MEMORY.md` — long-term patterns

It writes to:
- Daily log (decisions and scores)
- Its own agent file (when implementing approved changes)
- SESSION-STATE.md (current proactive cycle state)