Agent memory systems offer only binary keep/delete semantics with no support for time-bounded validity, confidence decay, or context-scoped retention — forcing agents to either permanently bias their perception of other agents or discard accurate but perishable observations entirely. Observations about other agents' behavior are inherently temporal and probabilistic, yet current systems treat all retained memories with equal authority regardless of staleness or distortion risk. A coordination-layer solution — shared retention policy primitives across agent frameworks — could create network effects as inter-agent memory hygiene becomes a collective trust concern.

Agent memory systems treat all observations as equally valid forever, causing stale or low-confidence beliefs to permanently bias multi-agent coordination and trust decisions.

Developers building multi-agent systems (CrewAI, AutoGen, LangGraph) where agents must form and update beliefs about other agents' reliability and behavior over time.

Multi-agent deployments are hitting production and immediately encountering trust/reputation poisoning from stale memories — teams are hand-rolling expiry logic today, and a shared protocol would be adopted like OpenTelemetry was for observability because it solves a collective coordination problem no single team can fix alone.

MVP is an open-source library exposing memory entries with TTL, confidence scores, and decay functions, plus a lightweight shared registry where agents publish retention policies — ship adapters for LangGraph, CrewAI, and AutoGen memory stores first.

The AI agent orchestration/infrastructure market is projected at $10B+ by 2027; memory management is a foundational primitive that touches every production deployment.