Context
Who requested the task, what constraints applied, and what environment was active.
Agent Memory Design
Episodic memory that improves agents without creating chaos
Episodic memory stores what happened during prior tasks: the context, decisions, evidence, actions, result, and feedback. The design challenge is deciding what deserves to be remembered and when it should be retrieved.
Answer in brief
Store structured episodes, not raw transcripts. Retrieve only episodes relevant to the current goal, apply retention and privacy policies, and evaluate whether memory improves task success rather than merely increasing context length.
Episode schema
Use these dimensions to make the architecture decision explicit and testable.
Decision trail
The chosen plan, important alternatives, and evidence used.
Outcome
Whether the task succeeded, failed, or required escalation.
Feedback
Human corrections, objective scores, and lessons that can generalize.
Write policy
Use these dimensions to make the architecture decision explicit and testable.
Selective capture
Write an episode after meaningful completion, correction, or failure—not after every message.
Compression
Store a structured summary plus pointers to source evidence.
Confidence
Separate verified facts from model interpretations.
Retention
Expire sensitive or low-value episodes based on purpose and policy.
Read policy
Use these dimensions to make the architecture decision explicit and testable.
Retrieval filters
Filter by user, tenant, task type, time, permissions, and confidence before similarity search.
Reranking
Prefer episodes with successful outcomes and recent validated feedback.
Use
Present memory as evidence with provenance, not as an unquestioned instruction.
Evaluation
Compare task success, correction rate, latency, and privacy incidents with and without memory.
Production checklist
Use this as a design-review gate before release.
- Define an episode schema.
- Record source provenance.
- Separate facts from inferred lessons.
- Enforce tenant and user boundaries.
- Add deletion and retention workflows.
- Measure whether retrieved episodes improve outcomes.
Continue learning
Place this pattern inside a broader agent architecture and operating model.
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