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An incident agent can help most by surfacing a fix that made a similar outage worse—not by issuing orders. In Poojitha Narkatpally’s September 29, 2026 DEV Community article, an agent retrieves prior incident memories and warns engineers about a tempting action: rolling back a deployment. The useful part is the warning as a lead to investigate. It is not proof that rollback is wrong in the current incident.
Why incident memories should include failed fixes
Postmortems often preserve what restored service, but the actions that failed can be just as useful to the next on-call engineer. A record that says a fix made matters worse—and under what conditions—can help an incident assistant flag a familiar risk before someone repeats it under pressure.
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Narkatpally describes a prototype whose memory bank contains 104 incidents drawn from Slack, Cloudflare, GitHub, AWS, Datadog, CircleCI and LaunchDarkly. The author reports that its Hindsight memory bank contained 759 world facts, 182 observations and 7,135 links. These are the author’s descriptions of the prototype and its contents, not independently verified measurements or evidence that the system works broadly in production.
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How the agent is prompted to say “don’t”
In the described implementation, incident memories that mention a “trap”—a fix that made an incident worse—receive a ranking bonus. A system-prompt instruction then tells the agent to explicitly warn against trap actions when retrieved incidents support a warning. The instruction calls for language like “DO NOT do X.”
#1 Best Overall
This approach depends partly on a literal keyword: a memory describing a harmful fix without using the word “trap” may not receive that ranking bonus. More importantly, retrieval identifies a resemblance, not a guarantee that the same action will have the same result. The author puts it plainly: “A trap in past incidents isn’t necessarily a trap in yours.”
What the checkout example shows—and what it does not
The article gives an illustrative checkout-service scenario: after a 06:31 deployment, about 12% of requests return 500 errors. The figures and response details below are part of the author’s example, not independently verified operational incident data.
Without retrieved incident memory
The author says the memory-free answer invented specifics, including a NullPointerException, a newly added promo-code field, log counts and a Helm revision. It then recommended a rollback. That answer illustrates a risk of unsupported specificity: details that sound diagnostic can be fabricated rather than grounded in available evidence.
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The memory-backed answer instead proposed dependency-capacity exhaustion—such as exhausted Redis or database connection pools—as a hypothesis. It warned against kubectl rollout undo on the reasoning that rolling back might reintroduce configuration without freeing the exhausted resource.
The warning does not establish that rollback would be harmful in this checkout scenario. The author says rollback can be appropriate, describes the proposed cause as a hypothesis with medium confidence, and notes that the answer also included potentially irrelevant material about BGP and systemd-networkd. The right use of the answer is to check whether the suspected dependency is saturated and whether the rollback would actually change the condition—not to treat the warning as a command.
What the reported evaluation establishes
Narkatpally reports a self-graded evaluation on 10 held-out incidents, comparing the assistant’s root-cause category with the dataset’s true-category label. The author reports 9 of 10 category matches with memory. Without memory, the author reports 0 of 10 fully correct, with 4 partial answers and 6 hallucinated answers; one memory run hit a rate limit and counted as a miss.
This small, author-graded result is not a general performance benchmark. It measures root-cause category matching on that set, not whether “DO NOT” warnings were justified. The article reports no separate trap-warning precision or recall, and recurring outage classes could make held-out cases resemble incidents retained in memory. The figures therefore do not establish how often the agent would warn correctly—or miss a relevant warning—in live operations.
How to use a remembered warning during an outage
- Verify the current symptoms. Check the live errors, dependency health, resource saturation and deployment changes rather than accepting details supplied by the agent unless they appear in real logs or telemetry.
- Inspect the retrieved precedent. Find out what conditions made the earlier action harmful and whether those conditions are present now.
- Compare the proposed action’s effects. For a rollback, establish which configuration or code would return and whether that change addresses the suspected failure mechanism.
- Keep the decision with the incident team. Treat the warning as a prompt for investigation and human judgment, not an automatic rule.
For teams evaluating this kind of feature, measure warning quality separately from root-cause classification. Track false positives—warnings against actions that would have helped—and false negatives—missed warnings about actions that made matters worse. Also examine whether retrieved incidents are relevant, whether unrelated material bleeds into answers, and whether uncertainty is communicated clearly.
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