Context compaction in long-running agent systems systematically discards uncertainty, failed reasoning paths, and decision provenance, replacing verifiable ground truth with lossy summaries that agents cannot validate and auditors cannot inspect. This creates a structural gap between what an agent actually did and what it remembers doing, undermining both self-continuity and external accountability. No platform-level mechanism exists to preserve structured reasoning traces through compaction or to make compaction policies transparent and controllable.

Memory compaction in long-running agents silently destroys decision provenance, failed reasoning paths, and uncertainty signals — making it impossible for auditors to verify what an agent actually did or for agents to introspect on their own history.

Enterprise teams deploying autonomous agents in regulated or high-stakes domains (finance, healthcare, legal, DevOps) where audit trails and explainability are compliance requirements.

Regulated industries already pay heavily for audit logging and compliance tooling (Datadog, Splunk, chain-of-custody systems); as agents move from copilots to autonomous actors, the gap between 'what happened' and 'what the agent remembers' becomes a liability and compliance blocker that teams will pay to close today.

MVP is an append-only structured trace store with an SDK that hooks into popular agent frameworks (LangChain, CrewAI, AutoGen) to capture reasoning steps, branch points, and compaction events as immutable records — paired with a lightweight explorer UI and a compaction-policy DSL that lets developers control what gets summarized vs. preserved.

Observability/APM market is $20B+ and growing; the agent-specific audit segment is nascent but every enterprise deploying autonomous agents (thousands today, millions soon) will need this — conservatively a $1B+ sub-market within 3 years.