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An LLM agent should consider saving information when it is likely to help with future work—not simply because it appeared in a conversation. Define a short list of capture triggers, then check scope, future usefulness, sensitivity, duplication, and contradictions before adding anything to the wiki.
Why a wiki needs capture rules
Durable memory and conversation history serve different purposes. OpenAI’s Agents SDK distinguishes reusable memory files from session memory, which preserves message history; its documentation says, “Memory lets future sandbox-agent runs learn from prior runs.” OpenAI’s agent memory documentation describes memory as a way to carry useful lessons between runs.
A transcript can retain what was said without deciding what deserves reuse. A wiki needs that extra judgment: capture information with a plausible future payoff, keep it in the right scope, and retrieve it only when relevant. A saved note records a past claim; it does not guarantee that claim is still true.
Five events that should trigger a capture check
Use triggers as prompts to evaluate a note, not as automatic permission to store everything. These categories are supported across official memory guidance, though individual products may implement them differently.
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1. The user corrects the agent or states a durable preference
Record a correction or preference when it is likely to affect later tasks. Examples include a repeatedly relevant formatting preference or a correction to an assumption the agent has made before. OpenAI identifies corrections and preferences as reusable guidance; Azure SRE Agent documentation also includes team preferences among possible user-memory candidates. Keep the note scoped to the person or team it describes.
2. A decision comes with rationale that constrains future work
Capture a decision when its reason will help the agent avoid reopening the same question or violating an established constraint. For example, a project may choose a particular interface because another system depends on it. Store the decision and its rationale together; a bare outcome is harder to apply correctly later. This is a practical policy derived from documented project lessons and operating rules, not a claim that every memory product has a dedicated decision trigger.
3. A project has a non-obvious, reusable fact
Save constraints, configuration details, architecture choices, or dependencies that a future task is likely to need and would not safely infer. Microsoft’s Azure SRE Agent documentation specifically lists problem constraints, configuration details, and non-obvious dependencies as useful persistent knowledge.
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4. A difficult task reveals a cause, fix, or pitfall
Preserve the parts of a debugging or incident investigation that can prevent repeated exploration: symptoms, root cause, successful steps, failed approaches, and pitfalls. OpenAI lists task summaries and project-specific lessons as memory candidates; Microsoft lists symptoms, successful steps, root causes, and pitfalls. Keep the lesson concise enough to retrieve and apply.
5. The user explicitly asks the agent to remember something
Treat a direct save request as a strong capture signal, but still apply privacy and scope rules. Cloudflare documents explicit memory additions, and Microsoft documents explicit save commands. Cloudflare’s guidance also describes automatic extraction, illustrating that systems may combine user-directed writes with other capture modes.
Run an acceptance check before saving
For each candidate, answer these questions before writing to the wiki:
Rank #3
- Is it in scope? Does the configured wiki cover this topic, project, user, or team?
- Will it help later? Is there a plausible future task where this information will change a useful answer or action?
- Is it appropriate to retain? Exclude sensitive or otherwise inappropriate information under the system’s privacy rules. Filtering is not foolproof: Google Cloud warns that sensitive-data exclusion in Memory Bank may not catch everything.
- Is it already recorded? Search the relevant topic page or index to avoid adding a duplicate.
- Does it conflict with an existing note? Determine whether the new information corrects, supersedes, or merely contradicts the old note. Update or flag the earlier record rather than silently preserving incompatible claims.
Google Cloud’s Memory Bank documentation describes topic filtering and consolidation that checks for duplicates and contradictions. Microsoft’s guidance describes updating existing files and removing stale or incorrect content. These controls are useful design patterns, not a guarantee that every memory system performs the checks automatically.
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Separate the index from the detailed notes
Keep a small orientation layer—a summary or index—with links to focused topic files. Add the accepted insight to the relevant page and update the index when needed, rather than creating an unlinked fragment for every conversation. OpenAI documents an injected summary, searchable memory index, and detailed summaries retrieved on demand. Microsoft describes an overview file that links to topic files.
Make scope visible
Label whether a note applies to a person, project, team, tenant, agent, or environment. A project-specific choice should not quietly become a universal rule. Cloudflare’s Agent Memory documentation describes profiles and namespaces that can isolate information for different users, agents, teams, tenants, or application entities.
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Retrieve detail only when it matters
Load the index or compact summary for orientation, then open a detailed note when the current task makes it relevant. Anthropic describes just-in-time retrieval from memory files, while Microsoft and OpenAI also document selective-access patterns. Anthropic’s memory tool is client-side: its documentation says, “The memory tool operates client-side: Claude requests file operations, and your application executes them.” That model gives the application control over storage and file operations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose capture and storage mechanics to fit the system
There is no evidence here for one universally best implementation or a single trigger threshold. Compare designs by how they capture, isolate, review, and retrieve information:
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- Capture mode: explicit writes, automatic extraction, or a hybrid. Cloudflare documents explicit additions and automatic extraction; Google Cloud documents generation triggered by events such as time, session boundaries, or event volume.
- Storage and control: workspace files, application-controlled file operations, or a managed memory service. OpenAI documents workspace memory files; Anthropic describes client-side operations with application-controlled storage; Cloudflare offers a managed service.
- Review controls: topic filtering, duplicate and contradiction checks, revision visibility, editing, and deletion. Google documents filtering and consolidation; Cloudflare lists APIs to add, list, recall, and delete memories.
- Retrieval design: an always-loaded summary, progressive disclosure, or just-in-time access to focused notes. The goal is to make relevant knowledge available without treating every saved detail as necessary context for every task.
Cloudflare’s Agent Memory page marked the service private beta and was last updated June 2, 2026; availability and product details can change. Check the current documentation before designing around a specific service.
Keep memory revisable and trust calibrated
Preferences change, projects evolve, and operational details expire. OpenAI warns that memory can become stale; Google Cloud and Microsoft describe updating or removing outdated, duplicate, or contradictory material. Keep volatile facts identifiable and reviewable. No universal expiry period or best time-to-live is established, so define review or expiration rules for the system’s needs rather than presenting one interval as a standard.
When a note is retrieved, treat it as evidence that someone recorded the information—not proof that it remains accurate. The agent-first memory architecture guidance highlights continuity, retrieval, decay, contradiction, and doubt as design concerns. If a decision depends on a volatile fact, verify it against the relevant current source before acting.
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