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AI automations lose context when a later step does not receive the state it needs from an earlier one. “Context” may mean conversation history, application data, external knowledge, or workflow progress—and those are separate things to store and pass. To fix a workflow, identify which state disappeared, where it was supposed to live, and the first handoff where it stopped reaching the next step.
What “context” means in an AI workflow
Before diagnosing an agent that seems to forget, separate four kinds of state. A workflow can preserve one while losing another:
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- Conversation history: messages and other items supplied to the model as part of a conversation.
- Run-local application context: structured data available to your code during a run. In the OpenAI Agents SDK, this is distinct from conversation state and is not automatically a durable transcript.
- External knowledge: facts the agent must retrieve from tools, search, files, or an authoritative data store.
- Workflow progress: what has been completed, what is pending, and what is needed to resume after a pause or restart.
The OpenAI Agents SDK describes these distinctions in its context management guide; its runtime guide documents separate ways to continue a conversation. A message can be present in history while a tool result, approval payload, or application object is absent from the next step.
Why AI automations lose context between steps
The next model call receives no continuation state
Making another model call does not, by itself, make that call aware of earlier turns. The application must provide replay-ready history or continue using the same session, conversation, or prior-response identifier. OpenAI documents these as distinct continuation approaches. Choose one for a given conversation unless you have explicit logic to reconcile them: combining local replay with service-managed state can duplicate context.
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The workflow resumes with a different or non-durable session
A session only helps if the resumed run can retrieve the earlier items from the same session and storage. The OpenAI Agents SDK session mechanism retrieves prior history before a run and stores new run items afterward. For work that must survive an interruption, use the same session or another session configured with the same ID and underlying storage. Microsoft’s agent design patterns also recommend external durable shared state for long-running tasks that need to resume, including the relevant progress and conversation history.
A handoff leaves out tool or application data
Do not assume every item in one agent’s run is broadcast to another. In Microsoft Agent Framework handoffs, user and agent messages are synchronized, while tool-related contents are not broadcast to other participants. Forwarding filters can exclude function calls, results, approval payloads, and other tool-control content. This is documented behavior for that framework, not a universal rule for every agent system. Check the actual handoff contract and pass required results explicitly in a concise, validated payload.
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Trimming or context limits remove a critical detail
Conversation history grows as the workflow accumulates messages, tool results, and intermediate output. Filtering, summarization, or a context limit may remove an item the next step needs. The OpenAI Agents SDK for Python supports customizing how retrieved history is combined with new input and limiting retrieved items. Microsoft likewise recommends compacting or selectively pruning history according to what the next agent needs. Preserve decisions, constraints, current values, and references deliberately, then inspect the assembled input to confirm they survived.
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The missing information belongs in a tool or data store
Conversation history is not a substitute for current or authoritative application data. The OpenAI Agents SDK context guide identifies instructions, run input, function tools, and retrieval or web search as ways to supply information to a model. Put stable policy in instructions, pass task-specific values in input or structured state, and fetch changing facts from their owning source when needed. A value available in run-local memory should not be assumed to persist into another run.
An approval paused the run before it completed
Some approval workflows return an incomplete result with pending interruptions and a resumable state snapshot rather than a final answer. Treat that as paused work: handle the interruption, retain the snapshot, and resume using the chosen persistence strategy. See OpenAI’s results and state guide.
Choose one way to continue a conversation
OpenAI’s runtime guide describes four common approaches. The right one depends on who should own storage and how much control the application needs; none is a universal winner.
| Approach | State ownership and control | What the application must do | Portability and resumption |
|---|---|---|---|
| Application-owned history | The application owns the conversation history. | Store the items and send the appropriate history on each call; decide what to include or trim. | Can be portable across workers if the application’s storage is shared and available to them. Resumption depends on that storage. |
| Persisted SDK session | The SDK session mechanism retrieves and stores history through its configured session storage. | Reuse the session, or configure a session with the same ID and underlying storage when resuming. | Resumption across workers requires access to the same underlying storage. |
| Server-managed conversation ID | The service manages conversation state associated with the ID. | Pass the same conversation ID on subsequent turns. | Depends on the service-managed conversation being available to the application; the application has less direct transcript management. |
| Previous-response ID | Continuation is linked to a prior response. | Pass the appropriate prior-response identifier on the next call. | Resumption depends on retaining and using the relevant identifier. The application should verify its own persistence needs. |
These approaches and their interaction are covered in OpenAI’s running agents guide. If you mix approaches, define which source of history is authoritative and reconcile it intentionally; otherwise, the same turns may reach the model more than once.
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Handoffs and bounded specialist calls solve different coordination problems. Microsoft’s Agent Framework handoff documentation distinguishes transferring task ownership from having a primary agent call a specialist as a tool.
| Pattern | Who owns the task? | Context implication |
|---|---|---|
| Handoff | The specialist takes over task ownership. | Define and verify what crosses the handoff boundary; do not assume tool-control items or application objects are included. |
| Agent as a tool | The primary agent remains responsible and calls a specialist for a bounded subtask. | The primary agent can select the relevant context for that call and decide how to use the result. |
For either pattern, specify the required inputs and outputs at the boundary. Include only what the receiving step needs, in a format it can validate.
Debug context loss at the first broken boundary
- Give each run and conversation a stable identifier. Record which store owns each piece of state: transcript, tool output, application data, approval, or progress marker.
- Inspect the next step’s actual inputs. Check the assembled model input, the session or conversation identifier, and any structured application state supplied to the code.
- Compare the prior output with what the next step received. Check messages, tool calls and results, approval records, files or references, and workflow progress separately.
- Trace filters and transformations. Look for history limits, summarizers, handoff adapters, worker boundaries, or other code that may have dropped or changed an item.
- Test a pause or worker change. Confirm that storage is durable and that a resumed run uses the intended session or saved state.
- Use traces or item-level records to locate the first mismatch. OpenAI’s result documentation describes diagnostics that can include tool and handoff records, raw model responses, guardrail results, and usage details.
Make the smallest reliable fix
Once you find the missing state, fix that boundary rather than adding the entire transcript to every prompt. Persist required tool results or progress in an appropriate store, pass a validated handoff payload, or retrieve authoritative data at the point of use. Keep only the history needed for the next decision, and verify that any compacted version still contains the task’s critical constraints and current values. For interrupted work, save the resumable state and continue through the same persistence path rather than treating an incomplete result as a completed turn.
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