An incident agent that remembers a failed fix is only useful if it remembers why the fix failed and under what conditions. “Restarting the pod didn’t work” is a trap if it hardens into a rule. The same restart may be correct next month, on a different version, with a different bottleneck. The design below stores the incident context, the attempted step, the observed result and the conditions it happened under. It treats retrieved history as evidence to test against the live incident, never as a script to replay.
This is a design guide, not a build report. It makes no claim about measured results from a specific system. It draws on Microsoft’s Azure SRE Agent documentation, Microsoft Security’s guidance on agent memory, and AWS’s agentic AI guidance, and it marks where the guidance stops and where this article’s own synthesis begins.
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The core rule: live evidence first, memory second
The agent should gather current telemetry and deployment context before it lets a retrieved case shape any decision. Memory answers “what happened before?” Only live data answers “is that what is happening now?”
Microsoft’s Azure SRE Agent documentation describes a flow along these lines: acknowledge the alert, query observability systems, correlate deployment history when it is connected, search memory for similar issues, form hypotheses, validate them against evidence, then propose or perform a fix depending on the configured run mode. PagerDuty, ServiceNow and Azure Monitor are listed as example incident platforms. That is a vendor’s description of its own product, not independent validation that agents perform well.
A vendor-neutral workflow
The steps below are editorial synthesis from that workflow and from the security guidance, not a product specification.
- Ingest and scope. Capture the alert, the affected service, severity, and a stable incident identity.
- Gather live context. Pull logs, metrics, traces, recent deployments and service topology from authorized sources.
- Retrieve similar incidents with their evidence. Surface what was observed and under what conditions, not just the remediation that was eventually applied.
- Form competing hypotheses. Test each against current observations. A past case supplies candidates; it does not settle the question.
- Recommend a reversible, scoped step. Anything beyond the agreed autonomy boundary needs human approval.
- Verify the effect. Check fresh telemetry after the action, then record the outcome on the incident.
- Escalate when evidence is thin, when memory conflicts with what you are observing, or when a safety boundary is reached.
What an incident memory should contain
Microsoft documents automatic capture of observed symptoms, steps that worked, root cause, and pitfalls to avoid. Its example of a failed strategy is: “Increasing memory limit didn’t help. The issue was CPU throttling.” The failure is paired with an explanation, which is what makes it reusable. The documentation also distinguishes structured persistent knowledge files from individual searchable memories. That example supports storing failed approaches with their reasons. It does not establish a general schema or show that any retrieval technique is best.
The following record structure is a recommendation synthesized from Microsoft’s memory categories and safety requirements. It is not Microsoft’s internal format.
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Rank #2
| Field group | What to store |
|---|---|
| Identity and scope | Service or resource, environment, time window, incident ID, relevant versions or deployment IDs |
| Observed evidence | Symptoms, error patterns, alerts, telemetry links, and the observations that supported or contradicted each hypothesis |
| Attempted action | The exact action or runbook step, who or what initiated it, and the approval or autonomy mode |
| Outcome | Worked, failed, made it worse, or inconclusive, plus the observation and time window used to judge it |
| Conditions | Topology, configuration, dependencies, versions and anything else that may decide whether the result transfers |
| Cause and confidence | Root cause only when established; label working hypotheses as such |
| Provenance and lifecycle | Source incident or thread, author or agent identity, timestamps, revision history, expiry or review state |
Store failures as conditional
Record a failed action as “did not help in this context,” not as a permanent prohibition, unless the evidence justifies the stronger rule. A sketch of what that looks like:
{
"action": "raise container memory limit",
"outcome": "did not help",
"judged_by": "error rate unchanged for 30 min after rollout",
"actual_cause": "CPU throttling (confirmed)",
"conditions": {"service": "checkout-api", "version": "2.14.1", "cpu_limit": "500m"},
"applies_when": "symptoms match AND cpu_limit/version are comparable",
"review_by": "next deploy of checkout-api"
}At retrieval time, the agent compares those conditions with the live incident. If the version, limits or topology have changed, the memory becomes a weaker hint rather than a veto. The values above are illustrative.
Retrieval is a security decision
Microsoft Security warns that persistent memory can influence later tool selection and reasoning, even in a different session or application. Its guidance is that memory should be treated as candidate context rather than authoritative truth: “Memory is candidate context, not authoritative truth.” It says to validate relevance and freshness, reevaluate sensitive or malicious content, stop memory from overriding safety controls, and guard against cross-context disclosure.
Rank #3
In practice, run these checks before a memory enters the agent’s working context:
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- Is the current requester allowed to see it? Isolate scopes by user, tenant, service or agent where needed.
- How old is it, and does it still match the current resource and versions?
- Does it contain text that reads like instructions? Treat that as untrusted input.
Give operators a way to inspect, correct and delete memories. When a retrieved incident materially shapes a recommendation, show which memory influenced it so the operator can challenge it.
Keep memory from granting authority
Memory informs an investigation. It should never widen what the agent is allowed to do. Define which actions the agent may recommend, which it may execute, and which need human approval. Keep the evidence and the policy decision behind each action so an operator can see why it was proposed and whether the agent had permission.
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Microsoft recommends logging memory create, read, update and delete operations with identity, timestamp, source and provenance. It also recommends tracking how memory propagates and retaining enough history for investigation and rollback, while watching logging costs, privacy and data minimization.
AWS’s guidance recommends attributable, tamper-evident, queryable decision records, captures the initiator of each action, and says to redact sensitive data before long-term storage. It names three anti-patterns for investigations: logging only final outputs, mutable logs, and unindexed artifacts. The AWS services mentioned there are options, not requirements.
A usable trail links the trigger to the retrieved memories, the evidence gathered, tool calls and results, approvals, observed outcomes and later memory changes. Keep secrets and unnecessary personal data out of the permanent record. Make sure the agent’s operational permissions cannot silently rewrite the evidence used to investigate the agent.
Best Value
Design choices to compare
| Axis | Options | What to weigh |
|---|---|---|
| Memory representation | Incident episodes with timelines vs. concise topic knowledge | Fidelity, retrieval relevance, maintenance effort, ability to preserve failed outcomes |
| Retrieval | Semantic similarity alone vs. hybrid or metadata-aware filtering | Whether results respect resource, version, time and environment. The sources reviewed do not establish a best method. |
| Trust controls | Write-time validation, retrieval-time screening, access isolation, operator review | Safety against operational friction |
| Autonomy | Recommend only, approval-gated, bounded automation | Response speed against the cost of a wrong remediation |
| Audit | Varying completeness and tamper resistance | Query speed, retention, privacy, rollback support |
How to evaluate it
Score each investigation on whether:
- the recommendation is supported by live evidence;
- the retrieved history was relevant and current;
- the right tools were chosen;
- a past failed action was described accurately;
- the action was authorized;
- the outcome was verified.
Microsoft lists memory-response accuracy and satisfaction, coverage of memory-specific threats, mean time to detect and remediate memory corruption, and availability of review, edit and delete controls as possible measures. AWS recommends evaluating correctness, helpfulness, tool-selection accuracy and safety. Neither gives universal target values.
The sources reviewed also do not show that persistent memory improves incident outcomes in general. Any claim such as “memory cut MTTR by X%” needs your own baseline, comparison group, time period and test conditions.
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