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Preserving agent context across sessions requires durable memory and structured continuity handoffs that capture active work and transfer it without loss. This approach uses frontmatter-based contracts to move context across threads while minimizing irrelevant data.

Proactive context protection is a mechanism that safeguards important memory during ingestion and recall. It enforces data hygiene by protecting secrets and ensuring auditability within stored memory episodes.

Ingesting and deduplicating external sources for agent memory involves applying a retrieval workflow that filters redundant data during capture. This builds a durable memory store by keeping operational knowledge and patterns while discarding duplicates.

OpenClaw supports durable working memory through automatic prompt-time memory injection and retrieval. This enables agents to access operational learning and captured failures dynamically during active sessions.

Building a RAG memory system for autonomous agents involves combining durable memory capture with proactive context protection. This method ensures continuity transfer and data hygiene while ingesting and deduplicating external sources.

Agents lose operational knowledge between threads due to missing continuity handoffs that transfer active work across sessions. Without structured memory capture, failures and patterns are not ingested into a durable store for later recall.