Developer builds 12-state tracking system to hold AI agents accountable for promises

A developer has built COGEXT, an accountability layer designed to track commitments made by AI agents through 12 distinct states, from detection to fulfillment or failure. The system addresses a core weakness in current AI agents: they generate intent readily but have no mechanism to track whether those commitments are ever fulfilled. Every extracted commitment is stored in PostgreSQL and transitions between states via a single atomic function, ensuring the audit log always matches the recorded status. A two-stage pipeline first classifies agent output to distinguish genuine commitments from suggestions or hypotheticals, then uses Meta's Llama 3.3-70B model to extract structured fields like action, recipient, and deadline. Fulfillment is verified through a weighted field-coverage model rather than simple keyword matching, requiring evidence to cross a confidence threshold before a commitment is marked complete.