48 lines
1.6 KiB
Markdown
48 lines
1.6 KiB
Markdown
# Proactive Agent Pattern
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A proactive agent observes its environment, identifies improvements, and
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self-modifies within strict guardrails. This pattern is inspired by
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OpenClaw's proactive agent skill.
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## When to use
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- Agents that run frequently and should improve over time
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- Pipelines with measurable performance metrics
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- Systems where the cost of not improving exceeds the risk of changes
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## When NOT to use
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- Simple pipelines that just need to run reliably
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- Human-in-the-loop workflows (the human provides the feedback)
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- New systems that haven't established a performance baseline yet
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## Components
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- **PROACTIVE-AGENT.md**: Agent template with proactive cycle, VFM protocol, self-healing
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- **ADL-RULES.md**: Anti-Drift Limits — constraints that prevent uncontrolled drift
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- **VFM-SCORING.md**: Value-First Modification — scoring rubric for proposed changes
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## How ADL and VFM work together
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ADL defines what the agent CANNOT do (hard boundaries).
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VFM determines what the agent SHOULD do (prioritization within boundaries).
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```
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Proposed change
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→ Check ADL constraints → BLOCKED if constraint violated
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→ Score with VFM → IMPLEMENT if > 50, DEFER if <= 50
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→ Log decision either way
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```
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## Integration with feedback loops
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The proactive agent reads from:
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- `feedback/FEEDBACK.md` — pipeline run outcomes
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- `budget/cost-events.jsonl` — cost data
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- `logs/audit.log` — tool call history
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- `memory/MEMORY.md` — long-term patterns
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It writes to:
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- Daily log (decisions and scores)
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- Its own agent file (when implementing approved changes)
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- SESSION-STATE.md (current proactive cycle state)
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