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What an agent memory handoff does
A handoff is a small, current map of work, not a replacement for the full record. It tells a later run what the task is, what has been decided, where the work stands, what should happen next, and where to retrieve supporting details.
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This pattern addresses the question: how can you shrink an agent’s context and still resume the work later? The answer depends on maintaining a path from the handoff to persisted state. A summary without that path may preserve a conclusion while losing the evidence and sequence needed to check or continue it.
Keep the three state layers separate
| Layer | Purpose | What it should contain |
|---|---|---|
| Working handoff | Orient the next run quickly | Task, decisions, current state, next action, and references to authoritative history or artifacts. This is a practical design pattern, not a vendor-prescribed schema. |
| Session history | Preserve interaction-scoped conversation state | The retrievable record for a particular session or conversation. In the OpenAI Agents SDK, a Session stores conversation history for a specific session and supports retrieval, append, pop, and clear operations. OpenAI Agents SDK sessions |
| Durable memory | Carry useful knowledge into separate future interactions | Selected stable facts, preferences, decisions, or reusable workflow lessons—not an implicit replacement for case-specific investigation history. OpenAI’s sandbox Memory feature distills lessons from prior runs into workspace files; Databricks describes durable memory in terms of facts, preferences, and decisions that remain useful in later conversations. OpenAI sandbox memory; Databricks agent memory |
Compaction is a fourth, related operation rather than another name for memory: it reduces active history so a long-running run can continue. The OpenAI cookbook distinguishes this within-run continuation from memory that carries reusable workflow lessons into future runs. OpenAI cookbook: session memory
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Choose how the later run will recover state
Before writing a handoff, decide who owns the underlying history and how a later run identifies it. SDKs and services differ: an application may reuse a stable session ID, retrieve a service-managed conversation, or save and restore serialized session state. OpenAI documents reusing SDK sessions across runs, while Microsoft Agent Framework documents serializing a session and deserializing it later to resume. OpenAI Agents SDK sessions; Microsoft Agent Framework: resuming sessions
- Who owns the record? Establish whether history belongs to the client, a service-managed conversation, or a custom backend.
- Where is it persisted? Identify the storage location and whether the history survives the process or machine that created it.
- How is it addressed? Store the session or conversation identity with the handoff so a later run can retrieve the correct record.
- What is authoritative? Distinguish the summary from source history, files, and other artifacts that substantiate the work.
Write a handoff that can be acted on
A useful note is concise because it omits transcript detail, not because it strips out the information needed to recover and verify the work. One practical structure is:
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- Task: the specific outcome being pursued.
- Decisions: choices already made and the reason when that reason affects the next step.
- State: what is complete, what remains, and any current blockers.
- Next action: the first concrete step for the receiving run.
- Recovery references: session or conversation identity plus paths or identifiers for relevant artifacts.
- Unverified assumptions: claims that the next run should confirm before relying on them.
Do not put a case-specific conclusion into durable memory as though it were a reusable fact. Keep investigation evidence in the session or its artifacts; use durable memory for knowledge that is genuinely useful across separate tasks.
Bound retrieval and protect the record boundary
Retrieving a full history can defeat the purpose of a smaller active context. Fetch only the portion needed for the next action, and keep the retrieved material within the model’s context limit. Microsoft’s storage guidance recommends session-scoped keys and bounded history; it also cautions against mixing locally maintained history with a service-managed conversation. Microsoft storage guidance for agent memory
Keep large or authoritative material—such as files and other task artifacts—outside the handoff text and reference it instead. The summary is an index and orientation aid; the underlying record remains the place to check details.
Choose when to compact
Compaction can happen automatically when a threshold is reached or explicitly at a phase boundary. The choice affects the run’s behavior as well as its context. The OpenAI JavaScript SDK guide notes that automatic compaction may delay completion after the final output token; it describes compacting between turns or during idle time as an option when lower perceived streaming latency matters. OpenAI Agents SDK JavaScript guide: context compaction
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For workflows where responsiveness matters, an explicit boundary can make the pause easier to place and explain. For workflows where uninterrupted execution is more important, an automatic trigger may be preferable. In either case, compaction should not erase the means to retrieve the underlying session or artifacts.
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A handoff can become wrong when work changes after it is written, when the wrong session identity is attached, or when a replacement summary is saved incompletely. Treat it as a pointer with a timestamp or version and verify it against the underlying record before continuing consequential work.
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- If the session cannot be retrieved, check the stored identity and persistence location before relying on the summary alone.
- If the session resumes but the summary conflicts with its history or artifacts, use the authoritative record and update the handoff.
- If compaction or replacement fails, preserve the prior recoverable state until the new state has been confirmed.
- If retrieved history is too large, narrow the requested range or select relevant artifacts rather than exceeding the context limit.
Questions to settle before deployment
- Is the handoff only an orientation note, or is the system incorrectly treating it as the complete record?
- Can a later run reliably associate itself with the right persisted session?
- Are session history, service-managed conversation state, and local state clearly separated?
- Are stable reusable lessons stored separately from conclusions tied to one investigation?
- Does the compaction policy account for both context capacity and the timing of visible completion?
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