Reflexive Metacognitive Agent
Agent maintains an explicit self-model of its own capabilities, confidence and limitations, and reasons over that model when accepting / refusing / handing off tasks.
Problem
Without an explicit self-model, the agent has no principled way to refuse tasks outside its competence or hand off to a more suitable peer. Refusals are ad-hoc, based on prompt-level instructions that are inconsistent across calls. Differs from confidence-reporting (which is per-output) by making the self-model an *input* to decision-making, not just an output.
Solution
Self-model is a structured artifact: {capabilities: [...], confidence-by-task-class: {...}, declared-limitations: [...]}. At task acceptance, agent reasons over self-model: does this task fall in my capabilities? what's my confidence for this class? are any declared limitations triggered? Output: accept / refuse-with-reason / handoff-to-peer-with-capability-X. Self-model refreshed periodically against eval-suite results. Pair with confidence-reporting, decentralized-swarm-handoff, refusal, typed-refusal-codes.
When to use
- Agent operates in a domain where competence boundaries are clear.
- Handoff to peer agents is feasible.
- Eval suite can refresh self-model periodically.
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