MetaCogAgent
MetaCogAgent equips a multi-agent system with metacognition, so each agent decides whether it should answer or delegate. The bottleneck in current multi-agent systems is over-delegation and under-delegation, and a metacognitive gate is a principled way to manage both. The Metacognitive Unit (MCU) at each agent produces confidence scores that drive routing to a delegation hub.
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Confidence-driven routing: Each agent's MCU combines verbalized and profile-based confidence into a single score. Low-confidence tasks route to a delegation hub rather than getting answered anyway.
Self-aware specialization beats fixed routers: MetaCogAgent reaches 82.4% on MetaCog-Eval, versus 70.2% for a skill-fixed router and 65.3% for single-agent. Self-assessment and adaptive delegation each contribute material gains in ablations.
Emergent specialization: Distinct confidence profiles (high on coding, low on retrieval, etc.) emerge purely from feedback. No specialization is encoded beyond initial system prompts.
Why it matters: Multi-agent systems usually rely on fixed routers or simple round-robin schemes. A learned, uncertainty-aware delegation gate gives a primitive that adapts to task difficulty without retraining the routing layer.