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To preserve an AI agent’s important state during compaction, put an application-level gate around the provider’s compaction mechanism. The gate should detect context pressure, request compaction when needed, validate a typed continuation checkpoint, and resume work only when required state passes validation. Compaction APIs and typed schemas provide useful building blocks, but neither OpenAI nor Anthropic prescribes a universal gate or checkpoint contract.
The key question is: “How do I keep an AI agent’s important state when its context gets compacted?” Keep application-local dependencies separate from model-visible conversation state, define which workflow facts must survive, and make invalid or incomplete checkpoints follow an explicit recovery path.
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Separate application context from model-visible context
Agent systems use “context” to mean two different things. Application-local context can contain dependencies and state used by tools and callbacks. Model-visible context is the material available to the model in its conversation history. OpenAI Agents SDK documentation states, “The context object is not sent to the LLM.” See the Agents SDK context documentation.
Represent those layers separately. An ApplicationContext can hold services, authorization decisions, and policy; a ContinuationCheckpoint can describe the minimum workflow state the model needs to continue. This separation is an application design choice, not a required SDK type. Do not serialize secrets, live dependency objects, or privileged application state into a prompt-facing checkpoint.
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What compaction does—and what it does not guarantee
Compaction is a way to continue a conversation with a smaller representation of prior context, not simply a rule for deleting old messages. OpenAI’s Responses API documentation describes a compaction item as carrying prior state forward using fewer tokens. Its standalone compaction endpoint returns a compacted window that should be passed forward as-is. OpenAI compaction guide.
Anthropic represents compaction with a block that must be retained in subsequent requests. That provider-specific continuation representation is not interchangeable with OpenAI’s. Follow the relevant provider’s instructions rather than applying a generic pruning or conversion rule. Anthropic context-window documentation.
These mechanisms do not define which facts your application must preserve, whether a returned state meets your policy, or what to do when it does not. Those responsibilities belong in the application gate.
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A schema turns the continuation contract into something the application can inspect. It does not decide which facts matter; that is a workflow and safety decision. A checkpoint might include:
schema_version: identifies the checkpoint format.task_goalandcurrent_phase: establish what the agent is trying to accomplish and where it is in the workflow.completed_workandpending_actions: distinguish finished steps from work that remains.user_constraints: preserve requirements that must continue to govern the task.relevant_referencesandunresolved_decisions: retain necessary source pointers and open questions.compacted_through: records the point in the conversation the checkpoint represents.
Classify fields as required, optional, stale, or safe to reconstruct. For example, if a user constraint limits permitted actions, make it required rather than hoping the model can infer it from a summary. Treat that classification as application policy, not a promise made by a structured-output feature.
The OpenAI Agents SDK supports typed context and structured output schemas, including local validation for supported schema types. Agents SDK agents documentation. Schema support helps enforce shape and types, but your code still needs semantic checks: a checkpoint can be syntactically valid while omitting a critical constraint or containing contradictory instructions.
Build the gate as an explicit state machine
A useful gate has four outcomes. The names below are proposed application states, not vendor API features.
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continue_without_compaction: context pressure is below the application’s trigger and no compaction is required.compact_and_validate: request compaction, parse the result, and check the checkpoint against the schema and policy invariants.repair_or_retry: the compaction response failed or the checkpoint is incomplete or invalid, but a bounded recovery attempt is appropriate.stop_for_review: the checkpoint remains unsafe or incomplete, the schema version is unsupported, or there is not enough headroom to compact reliably.
Do not treat a successful compaction response as proof that continuation is safe. Permit work to resume only after required fields and policy checks pass.
Choose when to compact without exhausting the window
Use measured token use and the actual model and request window to set a trigger. Reserve room for the compaction instruction and its response; account for input, output, and reasoning tokens where applicable. OpenAI notes that context limits cover input and output, and for some models reasoning tokens as well; excess generation can be truncated. OpenAI conversation-state documentation.
Do not assume one threshold is correct for every provider, model, or workload. The official documentation describes threshold-based mechanisms, but it does not establish a universal application threshold. Tune the trigger against the specific request window and the amount of headroom your compaction step needs.
Implement the gate in this order
- Measure pressure. Estimate or measure tokens for the actual request and compare the result with your configured trigger and available window.
- Keep going if there is headroom. Return
continue_without_compactionwhen the workflow can proceed without risking the window. - Request compaction using the provider’s mechanism. Use the appropriate provider API or threshold-based behavior; preserve its returned continuation representation according to provider instructions.
- Parse and validate the checkpoint. Check the schema version, required fields, constraints, unresolved decisions, and any workflow-specific invariants.
- Route failures explicitly. Send correctable omissions or transient failures through a bounded repair or retry branch. Stop for review if validation still fails or the state cannot be trusted.
- Resume only after approval by the gate. Pass the provider’s canonical compacted representation forward and allow tools or external mutations only when the checkpoint and application policy permit them.
OpenAI handoff documentation describes schema parsing and validation patterns and warns that authorization depending on parsed fields must be checked before application side effects. Applying the same conservative principle to compaction checkpoints is a design recommendation; it is not a vendor guarantee about compaction. Agents SDK handoffs documentation.
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| Design choice | Option A | Option B | Practical distinction |
|---|---|---|---|
| Control | Provider-managed threshold compaction | Application-triggered or on-demand compaction | Provider-managed behavior reduces custom trigger logic; application control makes it easier to align compaction with workflow policy. Exact behavior depends on provider support. |
| State representation | Provider compaction item or block | Application-defined typed checkpoint | The provider representation supports continuation; the typed checkpoint gives the application fields it can validate. One does not automatically replace the other. |
| Portability | Provider-specific continuation data | Application-owned checkpoint schema | Provider payloads are not established as interchangeable. An application-owned schema can express a portable contract, but provider-specific continuation instructions still apply. |
| Recovery | Provider-defined behavior where available | Application-defined repair, retry, or review path | There is no universal recovery policy in the cited documentation. Decide how failures, incomplete state, and schema changes affect continuation. |
Handle failure and observe the gate
Make invalid state visible and actionable instead of silently treating it as complete. A schema-version mismatch may require a migration or review. Missing user constraints may require another compaction attempt or a stop. A failed compaction response should not be interpreted as a valid checkpoint. If token headroom is insufficient for the compaction instruction and response, stop or use a designed fallback rather than assuming the operation will succeed.
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For operational telemetry, record the gate outcome, schema version, token estimate, compaction result, validation errors, and resume decision. Avoid logging sensitive prompt content or user data. These are implementation recommendations; the cited API documentation does not mandate a telemetry schema.
Test correctness across repeated compaction
Evaluate the gate against representative workflows, especially those where constraints, authorization, pending actions, or unresolved decisions matter. Check whether repeated compaction preserves required state, whether invalid checkpoints are blocked, and whether recovery paths behave as intended. Consider latency, token use, and task correctness as evaluation dimensions; there is no benchmark or universal success rate established by the cited documentation.
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