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An incident-response agent should remember prior investigations as evidence to consult, not as commands to repeat. A safe design records concise, sourced lessons; retrieves them alongside current runbooks and telemetry; shows why it recommends an action; and leaves consequential decisions under human review.
What should incident memory do?
Memory and runbooks answer different questions. A prior incident can reveal symptoms, a resolution that worked, a root cause established at the time, or a pitfall. A runbook describes the currently prescribed procedure. Current telemetry shows what is happening now. An agent needs all three, without treating any one as a substitute for the others.
| Information source | What it contributes | How the agent should use it |
|---|---|---|
| Prior incident memory | Past symptoms, investigated evidence, actions, outcomes, and known pitfalls | Use as a lead for investigation; check whether its conditions still match. |
| Authoritative runbook or knowledge base | Documented procedures and current operational guidance | Use to ground proposed procedures, checking that the document is relevant and current. |
| Current incident evidence | Present telemetry, alerts, logs, resource state, and investigation findings | Use to establish whether a past case is analogous and whether an action is justified now. |
Microsoft Learn’s Memory and knowledge in Azure SRE Agent describes past incidents and documentation as separate retrieval sources, with grounded answers, clickable citations, and links from insights back to their source threads. Its documented example query, “How did we fix this before?”, is useful precisely because the answer should point to history rather than silently convert history into policy.
Microsoft describes the intended value this way: “Your agent becomes more effective over time by remembering what worked in past incidents and referencing your documentation.” That is a vendor description of the product’s design, not independent evidence that it improves incident outcomes.
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What should a durable incident memory contain?
Store a compact incident record, not an unfiltered transcript. The fields below are a practical design recommendation based on Microsoft’s documented structured insights and source-linking approach; they are not a claim that every vendor uses this exact schema.
| Field | What to preserve |
|---|---|
| Identity and time | Source incident identifier, timestamps, service or resource affected, and relevant environment or region. |
| Observed symptoms | What responders actually saw, such as alert conditions or measured errors; distinguish observations from interpretation. |
| Evidence considered | Relevant logs, telemetry, configuration or deployment changes, and links to the investigation artifacts where available. |
| Cause and confidence | Root cause only when established, with supporting evidence. Mark unresolved causes as hypotheses rather than facts. |
| Actions and result | What was attempted, what worked or failed, the resulting state, and any known side effects or follow-up. |
| Provenance and lifecycle | Who or what created or updated the record, when, why, its source, and any review, expiry, or retention status. |
Keep the distinction between “the database recovered after failover” and “failover caused recovery” explicit unless the investigation established causation. Preserve failed actions and caveats as well as successful fixes; otherwise retrieval can present an incomplete precedent as a reliable recipe.
How should the agent use memory during an incident?
The operating loop is: understand the current incident, retrieve relevant history and authoritative guidance, propose a bounded response, gather evidence, and report what happened with timestamps and sources. Microsoft’s Step 4: Set up incident response in Azure SRE Agent, updated June 12, 2026, documents incident sources, response plans, memory retrieval, evidence gathering, and a recommendation to begin with Review autonomy.
- Establish the current context. Ingest the incident source and identify the affected service, resource, severity, and available evidence before searching for a precedent.
- Retrieve both history and guidance. Search for similar incidents and relevant knowledge-base or runbook material. Microsoft documents prioritizing exact-resource history as a relevance cue and keeping past-incident retrieval distinct from knowledge-base retrieval.
- Check whether the precedent still applies. Compare the prior case’s symptoms, resource, environment, dependencies, and relevant changes with current telemetry and configuration. A deployment or dependency change can make an old fix unsafe or irrelevant.
- Form a bounded plan. State the proposed action, its rationale, expected result, and supporting evidence. Identify whether support comes from a prior case, a current runbook, or both; do not imply that a precedent is policy.
- Gather and report evidence. After authorized steps, collect the resulting evidence and provide a timestamped account of observations, actions, and outcome. Keep links to the source incident and documentation visible where the system supports them.
- Update memory only after review. Distill the incident into a structured record, preserving uncertainty and provenance rather than automatically promoting every generated conclusion into durable knowledge.
For example, Microsoft’s related sample question is “How should I handle a database failover?” A useful answer would distinguish the current runbook’s prescribed failover steps from a prior incident in which failover was used, then cite current health evidence and explain whether the earlier circumstances match. The old case alone cannot authorize a failover.
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How much autonomy should the agent have?
Start in a review-oriented mode: let the agent retrieve, summarize, and propose while a responder checks the evidence and approves consequential actions. Microsoft’s incident-response tutorial recommends beginning with Review autonomy. The exact severity thresholds, approval roles, and actions that require authorization are organization-specific, so they should be set in the response plan rather than inferred by the agent from past incidents.
Organizations can route incidents by service and severity, with approval requirements proportionate to impact. A low-impact evidence-gathering step and a disruptive recovery action should not inherit the same permission merely because both appeared in a prior case. Record the authorization and the observed result so that later reviewers can reconstruct what the agent did and why.
How should persistent memory be secured?
Persistent memory is both information to protect and state that can shape later behavior. Microsoft Security’s June 22, 2026, Guarding AI memory describes a hypothetical delayed attack: malicious instructions are retained in memory and influence the agent in a different context later. The risk is not limited to the original incident or conversation.
- Control creation. Validate what can become durable memory, preserve its source, and prevent untrusted incident text from being treated as trusted instructions.
- Control access and retrieval. Apply access rules appropriate to the stored incident data and to the tools or actions an agent can invoke. Make retrieved content identifiable as historical evidence rather than system policy.
- Make changes auditable. Retain records that let investigators see what changed, when, why, and from where, including creation, edits, retrieval, and deletion where supported.
- Set retention and expiry. Define how long records remain useful, who can review them, and when they should be corrected or removed. Stale knowledge should not persist merely because it was once successful.
- Give people control. Provide a review and correction path for responders who find a misleading, unsafe, or improperly sourced memory.
These controls follow the threat model Microsoft describes across memory creation, storage, retrieval, model interaction, and user control. They are design requirements to evaluate, not a guarantee that any particular product implements every control.
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Google Cloud’s Agentic AI use case: Orchestrate security operations workflows documents an architecture example that combines retrieval-augmented grounding, Memory Bank, investigation artifacts, prior memories, telemetry, response plans, and specialist agents. It also describes saving investigation reports and new memories after analysis. This is a useful example of how historical context can sit alongside current evidence and prescribed procedures; it does not independently establish that the architecture is effective.
The Japan AI Safety Institute’s English-language Approach Book for AI Incident Response provides broader context for responding to AI incidents and systems whose state and external dependencies can change. It does not prescribe the incident-agent memory design described here. Its relevance is the reminder that response plans should account for changing system conditions rather than assume a model or its environment remains static.
What should teams assess when choosing or building a system?
Vendor documentation can show that a product documents particular features or an architecture; it is not an independent comparison or proof of measured effectiveness. The sources described here provide no comparative benchmark or quantified improvement in response time, incident reduction, or success rate. Assess the operational behavior directly against your requirements.
- Retrieval quality: Can the system find analogous incidents and give appropriate weight to exact-resource history without treating similarity as proof?
- Source separation: Are memories kept distinct from authoritative runbooks and current evidence?
- Provenance: Can responders inspect the source incident, cited documents, evidence, and timestamps behind a recommendation?
- Operational integration: Can it access the relevant incident sources, telemetry, investigation artifacts, and change information needed to check whether a precedent still applies?
- Memory safeguards: Are access control, poisoning defenses, retention, expiry, correction, and audit trails defined and visible?
- Human control: Can autonomy be limited by incident context, with review and explicit authorization for consequential steps?
Test with cases where a past fix remains appropriate, where a superficially similar incident has a different cause, and where a once-valid procedure has been superseded. Check whether the agent exposes uncertainty, cites evidence, and pauses for approval where required. Treat those checks as acceptance criteria, not as a substitute for ongoing review as services and dependencies change.
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