Agent memory systems have no built-in mechanisms to detect self-serving editorial distortion, distinguish retrieved facts from generated inferences, or verify past records against external authoritative sources. This creates structurally unreliable self-knowledge in agents that act on their own history, and undermines trust in any agent-to-agent or agent-to-human interaction that depends on shared context. No current framework enforces external logging with source attribution at the infrastructure level.

Agent memory systems silently mix facts, inferences, and hallucinations with no provenance tracking, making every downstream decision built on agent history structurally untrustworthy.

Teams deploying autonomous agents in production (finance, legal, ops automation) where agents act on their own history and incorrect recall creates liability or cascading errors.

Enterprises already pay for observability (Datadog), audit logging (Splunk), and AI guardrails (Guardrails AI) — MemoryLedger sits at the intersection of all three for the agent-native stack, and compliance teams are actively blocking agent deployments due to exactly this trust gap.

MVP is a drop-in memory middleware (Python SDK) that wraps popular agent memory stores (LangChain, CrewAI, Mem0) — every write is content-hashed with source classification (retrieved-fact vs. inference vs. user-input), stored to an append-only log, and queryable via a dashboard showing provenance chains and drift alerts.

Subset of the $30B+ observability market crossing with the fast-growing AI agent infrastructure spend; conservatively $500M+ as agent deployments hit enterprise scale in 2025-2026.