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An on-call assistant is less likely to suggest “we already tried that, it didn’t work” when its incident memory records what happened and whether each attempted fix helped. Girish Kumar Houdekar’s demonstration pairs incident retrieval with outcome records, then uses those records to inform suggestions for a new alert. It illustrates a useful design pattern—not proof that AI incident response is production-ready or safe to automate without an engineer.
What the on-call agent remembers
Houdekar describes a small application built with Hindsight for memory, a large language model served through Groq, and a Streamlit interface. Its incident records include an ID, service, date, symptom, root cause, attempted fix, and outcome. That last field is important: a log that says only “restarted the service” cannot tell a later responder whether the restart worked, failed, or made things worse.
The intended loop is straightforward: an engineer submits an alert, the application recalls similar incidents, and the alert plus retrieved records go to the language model. The model returns a diagnosis and ranked possible fixes, including actions that prior records mark as failures. After the incident is resolved, the operator retains a new record with its outcome. Houdekar characterizes this as a write followed by a read, rather than model retraining or a batch job.
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Houdekar reports seeding the demonstration with 10 synthetic incidents across five services. In one payments connection-pool example, the version without incident memory suggested restarting, despite the seeded history marking restart attempts as failures. With memory enabled, it reportedly recalled four related incidents, identified them by incident ID, and suggested rollback or configuration reversion based on records marked successful.
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A separate email-queue example reportedly recalled another pair of incidents and suggested failover rather than scaling workers, which the synthetic examples said had worsened the issue. These are author-reported demonstrations, not production measurements, an independently reproduced test, or evidence of reduced response time.
Why recording outcomes helps—and where it can mislead
Keep the result attached to the action
Record the symptom, suspected or confirmed cause, action taken, and observed outcome together. Preserve enough context—such as service and incident ID—to let an operator inspect the original record. A memory that labels a fix “failed” without explaining the circumstances can be as misleading as no memory at all.
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Check that a recalled incident actually matches
Similarity retrieval can surface a memory that sounds related but is not relevant. Houdekar says a vague input retrieved a payments-related incident and led to a confident but mismatched answer. His proposed safeguard is to ask for a specific service or symptom rather than force a recommendation from a weak match. In practice, the assistant should make its supporting records visible and allow the responder to reject them when service, symptoms, or conditions differ.
Treat prior failures as evidence, not rules
A failed action in one incident does not mean that action is always wrong. Houdekar deliberately included a stale signing-key incident in which restarting was the appropriate response, despite other records where restart had failed. The useful lesson is conditional: compare the current situation with the circumstances and outcomes in prior records, rather than turning a remembered failure into “never restart.”
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What the example does—and does not—establish
The project shows how stored incident outcomes can be presented to a language model alongside a new alert. It does not establish that the assistant diagnoses incidents accurately across real services, that its recommendations are safe, or that it improves incident response. The counts—10 seeded incidents and four recalled in one example—describe Houdekar’s setup and a single demonstration detail, not an accuracy rate or industry benchmark.
Hindsight’s official documentation describes retain, recall, and reflect operations, client libraries, self-hosted deployment, and a managed Hindsight Cloud option. Its Quickstart demonstrates retaining content, recalling matching memories, and generating a reflective response. Those materials document software capabilities; they do not validate this application or guarantee correct recommendations. See the Hindsight project documentation and Hindsight Quickstart.
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Design checks before using incident memory
- Store outcomes with the action and incident context, not as isolated labels.
- Show which incident records informed a suggestion so an engineer can assess their relevance.
- Ask for more detail or abstain when the alert is too vague to match reliably.
- Keep an engineer in control of remediation; the demonstration is not evidence for unattended changes.
- Choose self-hosted or managed deployment based on operational needs; Hindsight documents both, but the choice does not validate the recommendation logic.
Implementation detail from the Streamlit example
Houdekar notes that Streamlit reruns did not work well with a cached asynchronous client in this implementation, so the application created a fresh Hindsight client on each call. That is a project-specific integration workaround, not a general requirement for Hindsight or Streamlit applications.
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Houdekar writes, “The interesting part isn’t the plumbing, it’s what you choose to remember.” For an on-call assistant, remembering outcomes makes prior incidents more actionable—but only if retrieved examples are relevant, inspectable, and treated as evidence for a human responder rather than instructions to follow blindly. The account is Girish Kumar Houdekar’s first-person demonstration, published September 29, 2026, on DEV Community.
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