Agent Confidence Can't Be Static: Why Agents Need Dynamic Memory

Most agent systems treat confidence as immutable data, leaving outdated assertions intact even when contradicted by new evidence. This article proposes computing effective confidence dynamically from evidence graphs—support and challenge edges automatically adjust belief strength without retraining or human intervention. For production AI systems, this fundamentally improves reliability: agents with self-correcting memory resolve contradictions in real time, dramatically reducing hallucinations.