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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsIf an AI agent forgets an instruction or gives an outdated answer, first check what the model actually received on the failing turn. Then verify that the expected conversation or memory state persisted, that current sources were retrieved, and that no context or configuration error prevented the run from using that information. These are separate failure modes—not one universal “memory” problem.
Why an AI agent forgets instructions or uses outdated information
A model can use only the information made available to its call. OpenAI’s Agents SDK puts it this way: “When an LLM is called, the only data it can see is from the conversation history.” Instructions, user input, tool results, retrieved documents, and search results can all supply information, but a fact saved somewhere outside the call is not automatically visible to the model. See OpenAI’s Context management guide.
“Memory” can refer to different mechanisms with different continuity requirements:
- Current-run context: the instructions, input, history, and tool or retrieval results assembled for a particular model call.
- Conversation history: prior messages carried forward through an SDK session, application-managed history, or provider-managed continuation.
- Longer-term memory: selected facts or lessons distilled from earlier activity and stored for later retrieval. It is not necessarily the full transcript.
Storage and context behavior varies by platform. The checks below use OpenAI Agents SDK and API documentation as concrete examples; confirm the equivalent mechanisms and labels for your agent platform.
#1 Best Overall
1. Confirm the agent received the instruction
Inspect the exact input assembled for the model call that produced the bad answer—not just the chat screen or the original prompt. Follow the request through your orchestration layer and check whether a later call omitted, truncated, or summarized away the instruction.
- Review the system or developer instructions and the user’s latest input.
- Check which earlier messages were included and whether a history summary retained the relevant detail.
- Inspect retrieved passages, web results, and tool outputs actually supplied to the call.
- Compare the failing call’s rendered input with a successful turn, if available.
For example, an instruction visible in an application’s interface may never reach the model if the application builds a new request without it. Likewise, a tool may save a fact successfully while the next call receives neither that fact nor a way to retrieve it.
2. Check conversation identity and persisted history
Conversation continuity depends on the mechanism your application uses. In the Agents SDK, sessions store and retrieve history for a specific session; later turns need to use the same session identity or another session instance backed by the same store. The SDK’s Sessions overview describes this history mechanism.
Compare the successful and failing turns and verify that the application carried forward the intended state:
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- If your application assembles history itself, inspect the exact list passed to the model, including the output of methods such as
to_input_list()where applicable. - For provider-managed continuation, confirm that the correct conversation or previous-response identifier is supplied.
- If you use both application-managed history and server-managed continuation, check for duplicated messages. OpenAI’s Running agents guide warns that mixing these approaches can duplicate context unless the application reconciles them.
Starting a new session can explain why an agent no longer has an earlier conversation: the new session may not point to the old history or its underlying store. A session ID alone is not proof that the expected messages were loaded; inspect the history that reached the model.
3. Distinguish transcript history from longer-term memory
Ask what you expect the agent to retain: every prior message, a compact summary, or a small set of facts and preferences. A system that preserves conversation messages behaves differently from one that distills selected information into files or an index.
Rank #3
OpenAI’s Agent memory guide describes a flow that can summarize prior workspace runs and search an index for more detailed notes. The Sandbox Agents documentation distinguishes conversational history from distilled memory and makes continuity dependent on preserving the relevant workspace or artifacts. A fresh, empty sandbox does not contain an earlier workspace’s memory.
Trace the full memory path: did the earlier run write or update the artifact, did it survive session closure or restart, and does the next run actually read or retrieve it? If an agent can retrieve a summary but not the supporting detail, it may have incomplete context. Treat older notes as potentially stale and check them against the current environment before relying on them.
Because stored conversation records can include user inputs, assistant and tool items, interruptions, and outputs, review access controls and retention policies before persisting them. The Sandbox Agents documentation describes the kinds of records that may be retained.
4. Check for context or configuration errors
Look at the actual API and session errors for a failed turn. A context-length failure can prevent the intended request from completing; an oversized configuration can also be a problem. OpenAI’s Errors and recovery guide documents the context_length_exceeded error identifier, but it does not establish a universal context-window threshold or show how common such failures are.
- Reduce irrelevant history or other excess input while keeping the instruction and facts needed for the task.
- If the agent configuration is too large, review the instructions and tool definitions for unnecessary material.
- Before retrying a failed run, retrieve its session state and inspect completed tool actions. A run may have done work before an error or interruption, so blindly replaying it could repeat an action.
5. Verify source freshness and instruction authority
For an outdated factual answer, trace the retrieval path: check the query, the source and its publication or version date, and whether the returned material was included in the model call. Then verify that the newest authoritative source was available and selected. Search capability alone does not establish that a current source was found or used.
Also inspect retrieved pages, files, and tool results for instructions that conflict with the user’s request or the agent’s governing instructions. OpenAI defines prompt injection as a third party injecting malicious instructions into the conversation context. Its Understanding prompt injections guidance recommends limiting access and giving the agent a specific task. Treat external content as data to evaluate, not as authority to override the task.
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6. Reproduce the problem and change one factor
Once you know which state or retrieval path is involved, isolate it with a controlled test. This makes it easier to tell whether the failure comes from missing instructions, missing history, stale sources, or an error rather than changing several mechanisms at once.
- Reproduce the failure with a short, known instruction and a controlled conversation history.
- Log the model-visible input, retrieved passages, session or continuation identifier, model and tool calls, errors, and state writes.
- Choose one variable to change, such as persistence identity, retrieval freshness, history size, or instruction placement.
- Run the same test again and compare what reached the model and what state was written.
This is a practical diagnostic method, not a standardized test prescribed by the cited documentation. Avoid inferring a platform-wide memory behavior from a single reproduction.
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