Agent State Management

State is the structured representation of a run at a particular point. It may contain the original goal, accumulated evidence, completed steps, current ownership and references to external artifacts. A transition reads that state and produces an update; a persistence layer may save a checkpoint for later recovery. This differs from long-term memory, which carries selected information across separate tasks or conversations. Conversation history is only one possible state component and often cannot express transactional status reliably. Durable state also needs identifiers that connect resumed execution with the right user, run and external operation.

The practitioner defines a state schema, transition rules and which fields may be updated concurrently. Checkpoints should be stored durably when recovery matters, with retention and access controls appropriate to the data. Tools with side effects need idempotency keys or reconciliation logic because a restarted step can run again. Useful artifacts include a transition model, checkpoint records and recovery tests. The system should expose whether it is executing, waiting for input, failed or complete, rather than deriving every status from model-generated prose.

Persisting all messages indefinitely creates storage, privacy and context-management problems. A checkpoint can restore application state while the external world has changed, so resumed work may need fresh observations. Concurrent updates can overwrite each other without explicit merge semantics. Quality requires meaningful recovery behavior and consistent external effects, not merely successful serialization. State migrations also matter: a saved run from an earlier schema version must be interpreted or rejected deliberately when the application changes.