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To shrink an agent’s active context and still resume work later, keep a short handoff note for the next action, preserve the full interaction history in a recoverable session, and store only reusable lessons in durable memory. These are three different layers—not three names for the same summary.
What an agent handoff needs to preserve
A useful handoff reduces what the next run must load without making the summary the only surviving record. Treat it as a compact map: it tells the next agent what the task is, what has been decided, where work stands, what to do next, and where to retrieve supporting detail.
The following three-layer design is a practical synthesis of documented session, compaction, and memory mechanisms; it is not a prescribed vendor schema.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Working handoff: a brief, current note with the task, decisions, present state, next action, and references to relevant files or artifacts.
- Session history: the interaction-scoped conversation record, which can be retrieved or restored when the later run needs details omitted from the handoff.
- Durable memory: selected stable facts, preferences, or workflow lessons intended to remain useful in separate future conversations.
Keep case-specific investigative conclusions in the session record or their source artifacts. They should not silently turn into durable memory that a later conversation treats as universally true.
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How session history, compaction, and memory differ
Session history records an interaction
The OpenAI Agents SDK describes a Session as conversation history for a specific session, with operations to retrieve, append, pop, and clear items. Applications can reuse a session across runs, but the record’s scope remains the interaction. OpenAI Agents SDK: Sessions
Compaction reduces active history
Compaction condenses or otherwise reduces the history being carried in the active run so a long-running task can continue within context limits. It does not, by itself, establish a durable knowledge store for later, separate tasks. The OpenAI cookbook distinguishes compaction for continuing one run from memory that carries reusable workflow lessons into future runs. OpenAI cookbook: Session memory and compaction
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Durable memory carries selected knowledge forward
OpenAI’s sandbox Memory feature describes distilling lessons from prior runs into workspace files. Databricks describes durable memory as facts, preferences, and decisions that remain useful in later conversations. Both are different in purpose from preserving every turn of a particular session. OpenAI Agents SDK: Sandbox memory Azure Databricks: Memory
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Choose how a later run will recover the record
A handoff is only recoverable if the application knows what record to retrieve and where it lives. SDKs and services differ in who owns persistence, so make ownership and identity explicit in the design.
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| Recovery approach | What it restores | Design consideration |
|---|---|---|
| Reuse a stable session identity | Conversation history associated with that session | OpenAI Agents SDK sessions can be reused across runs; retain and correctly associate the session identity. OpenAI Agents SDK: Sessions |
| Retrieve a service-managed conversation | History maintained by the service for its conversation | Follow the service’s conversation identity and storage model. Do not mix application-managed local history with a service-managed conversation for the same interaction. Microsoft: Threads, runs, and messages Microsoft Agent Framework: Session storage |
| Serialize and later deserialize session state | The saved session state needed to resume | Microsoft Agent Framework documents serializing a session and deserializing it later; the application must persist the serialized state and retrieve the right copy. Microsoft Agent Framework: Session storage |
These are implementation options, not a universal ranking. The right one depends on who owns the history, where it is persisted, how a later run is associated with it, and what state the chosen mechanism actually restores.
Set compaction boundaries without hiding the source record
Compaction policy affects both continuity and responsiveness. An automatic trigger can manage long histories without an application choosing each boundary. Explicit compaction lets the application compact at a meaningful phase boundary, such as after a milestone, or during idle time. In the OpenAI JavaScript SDK guide, automatic compaction may delay completion after the final output token; the guide describes calling compaction between turns or during idle time when lower perceived streaming latency matters. OpenAI Agents SDK: Compaction
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Whichever policy is used, retain a way to get back to the underlying session or artifacts. The compacted context is a working representation, not necessarily a complete, authoritative record. Bound retrieved history to the model’s context limit, and retrieve only the detail the next step needs. Microsoft Agent Framework: Session storage
Build a handoff that can be acted on
- State the task and current objective. Make clear what the agent is trying to accomplish, not just the topic of the conversation.
- Record decisions and current state. Include confirmed decisions, completed work, unresolved questions, and assumptions that still need validation. Distinguish a confirmed result from a working hypothesis.
- Name the next action. Give the next run a concrete step rather than an open-ended instruction to continue.
- Point to source material. Include stable session identity or recovery instructions and references to the files or artifacts containing evidence or work products.
- Keep durable memory selective. Promote only stable preferences or reusable workflow lessons; leave task-specific evidence and conclusions with the session or artifact they belong to.
- Check recovery and context bounds. Verify that the later run can resolve the identity, retrieve the intended history, and fit the selected material within the model’s context limit.
These fields are a useful handoff pattern, not a vendor-mandated format. The important property is that a concise note points back to retrievable state instead of pretending to replace it.
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Prevent stale or conflicting state
Before a resumed run acts, it should be able to distinguish current instructions from old context and locate the source for consequential claims. Session-scoped keys help associate history with the right interaction, while bounded retrieval avoids loading more than the model can handle. Microsoft’s storage guidance also cautions against combining local history with a service-managed conversation. Microsoft Agent Framework: Session storage
- Use one clearly identified owner for each conversation’s history.
- Keep durable memory separate from case-specific conclusions and evidence.
- Use artifact references for detail that should remain outside the active prompt.
- When a replacement or update fails, do not assume the old record was replaced; verify which persisted session or artifact the next run will load.
The final check is operational: can a later run find the intended history, tell which facts are current, and retrieve supporting detail without treating the summary as the complete record?
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