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)

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