Manage conversations, checkpoints, retrieval indexes, artifacts, and memory as governed production data.
◷ Estimated reading time: 4 min
GenAI systems produce more state than chat history. They may store retrieved evidence, tool results, plans, checkpoints, generated artifacts, feedback, and long-term memories. Each store needs a purpose, owner, retention rule, and tenant boundary.
Separate Operational State From Model Context
The full system record should not be copied into every model request. Keep authoritative workflow state in application storage, then assemble only the context needed for the current step. This improves reliability, privacy, and cost.
Design the Lifecycle
Conversation store: user-visible history and audit references
Workflow store: step state, checkpoints, locks, and resumability
Retrieval store: source documents, chunks, embeddings, metadata, and index versions
Memory store: selected durable facts with provenance and deletion support
Artifact store: reports, code, files, and intermediate outputs
Define retention, access, encryption, backup, restore, migration, and deletion behavior. A model-generated memory should not become permanent merely because it was written once.
Data rule
Memory is a data product: it needs provenance, confidence, access control, expiration, and correction.
Key Takeaways
Operational state should remain authoritative outside the prompt.
Different stores serve conversation, workflow, retrieval, memory, and artifact needs.
Every persistent memory requires governance, provenance, and deletion behavior.
Why keep workflow state outside the model context?
Which property is most important for long-term AI memory?