Giving an SRE Incident-Response Agent a Real Memory with Hindsight
The problem When a production service falls over at 3 a.m., the slowest part is rarely the fix — it's the remembering. "Didn't we see this connection-pool exhaustion last quarter? What did we change?" That knowledge lives in scattered postmortems, Slack threads, and a few senior engineers' heads. Most AI incident assistants don't help here, because they're stateless: every incident starts from…
Incident response agents often struggle to recall past incidents and their resolutions, especially when a production service fails at odd hours. Knowledge about previous issues like connection-pool exhaustion is scattered across postmortems, Slack threads, and senior engineers' heads. Most AI incident assistants are stateless, meaning they start every incident investigation from scratch without any prior knowledge. This lack of memory is crucial for effective incident response.
EpistemicOps is an SRE incident-response agent that addresses this issue by incorporating a persistent memory layer using Hindsight, Vectorize's agent memory system. Hindsight integrates a bounded LangGraph agent that processes deterministic incident fixtures, generates a structured diagnosis, writes a postmortem back into Hindsight, and consolidates past incidents into an evolving runbook. The next similar incident can leverage this prior knowledge for a more efficient investigation.
Hindsight is central to EpistemicOps' architecture, handling memory retention, recall, consolidation, and creating a Mental Model runbook. The agent's architecture is a small acyclic state machine comprising read-only evidence tools for logs, metrics, traces, and pod status; a LangGraph agent interacting with Groq's openai/gpt-oss-120b for diagnosis; and Hindsight managing memory operations.
The agent follows a fixed sequence of loading incidents, querying memory, investigating the issue, analyzing the problem, validating the diagnosis, producing a result, retaining the postmortem, and concluding the process.
Cold mode initiates an investigation with no relevant memory, while a warm mode recalls a prior postmortem when a related incident occurs, injecting that context into the diagnosis. Incidents in EpistemicOps are deterministic JSON fixtures containing believable logs, metrics, traces, and pod status. The dataset consists of three incidents from the Apache-2.0 quantranger/sre-agent-eda-bundle dataset (with attribution) and two hand-authored incidents.
The hidden ground truth for scoring remains strictly isolated from the agent's view. The tools used are read-only, and no real infrastructure is touched.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
