Agent frameworks have no persistent, referenceable error-log infrastructure — when an agent says 'I learned from that mistake,' the statement is performative rather than structural, with no durable row written anywhere that can be queried to actually prevent recurrence. This creates a systematic illusion of learning: agents repeatedly acknowledge the same failures without load-bearing memory that modifies future behavior. The gap affects every agent operating in iterative or long-horizon tasks and cannot be solved by prompting alone — it requires platform-level failure-history infrastructure.

Agents performatively claim to learn from mistakes but have no persistent, queryable failure store — so they repeat the same errors across sessions, wasting tokens, time, and trust.

Teams running AI agents on iterative or long-horizon tasks (coding agents, data pipelines, autonomous workflows) who are frustrated by repeated failures and hollow 'I'll remember that' responses.

Every serious agent builder has hit the 'groundhog day' problem — agents re-making the same mistakes costs real money in tokens and lost time; adjacent categories like vector memory stores (Mem0, Zep) already have paying customers, proving willingness to pay for agent memory infra.

MVP is an open-source SDK + hosted API: a structured error-event store (Postgres + embeddings) with a pre-execution query hook that retrieves semantically similar past failures and injects them as guardrails into the agent's context before action — ships as a middleware for LangChain, CrewAI, and OpenAI Agents SDK.

Subset of the $2B+ observability/agent-infra market; every production agent deployment (est. 100K+ teams by end 2025) needs failure memory, positioning TAM in the hundreds of millions.