AI INCIDENT RESPONSE AGENT POWERED BY HINDSIGHT
AI Incident Response Agent Powered by Hindsight The most dangerous thing an incident tool can do at 3 a.m. is confidently repeat last month's fix.
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I built RecallOps around that problem: an incident
I built RecallOps around that problem: an incident response agent that remembers what happened before, but is explicitly designed not to treat that memory as the answer.
RecallOps is a small Python service with a
RecallOps is a small Python service with a Streamlit front end. An on-call engineer describes a production incident in plain text, selects the affected service and severity, and starts an investigation. Behind that button, three things happen: The incident description is used as a query against a long-term memory store. The recalled past incidents, together with the new incident, are given to a reasoning model. The model returns a structured analysis with a fixed set of sections.
After the incident is resolved, the engineer records
After the incident is resolved, the engineer records what actually fixed it and what the outcome was. That experience is written back to the same memory store, so future investigations can use it as additional context. The core implementation is split across the agent logic, Streamlit interface, and memory seeding code:
The memory layer is Hindsight, an open-source agent
The memory layer is Hindsight, an open-source agent memory system. The reasoning model is openai/gpt-oss-120b served through Groq, with a temperature of 0.2. I kept the temperature low because this application needs consistent reasoning rather than creative output.
I chose not to build my own memory
I chose not to build my own memory layer. Building a basic vector-search memory system is possible, but I wanted to spend my time on the agent's behavior rather than maintaining the retrieval infrastructure.
The more interesting problems come later: deciding what
The more interesting problems come later: deciding what to keep, how to retrieve it from a messy incident description, and how to keep stored experiences useful as they grow.
For this project, I used Hindsight's documentation as
For this project, I used Hindsight's documentation as the integration contract and kept the memory interaction focused on two operations: The Through-Line: Memory Is Evidence, Not an Answer Here's the failure mode I designed against.
You give an LLM a new incident and
You give an LLM a new incident and a retrieved past incident that looks similar. The model sees a familiar symptom and a previous fix and says: "Increase the connection pool from 100 to 200." "Increase the connection pool from 100 to 200." But similar symptoms do not necessarily mean the same root cause.
Payment timeouts with database connection errors might indicate
Payment timeouts with database connection errors might indicate connection pool exhaustion. They could also come from a slow query holding connections open, a downstream dependency becoming slow, or a deployment that caused connection leaks.
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AI INCIDENT RESPONSE AGENT POWERED BY HINDSIGHT
AI Incident Response Agent Powered by Hindsight The most dangerous thing an incident tool can do at 3 a.m.
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Source: Dev.to