Codifying Tribal Knowledge: How to Build Long-Term Memory for AI SRE Agents

Every AI root-cause tool wakes up with amnesia. Run it on the same recurring failure a hundred times, and it re-derives the same conclusion a hundred times – paying full price each round and never getting wiser about your environment. Meanwhile, the knowledge that actually resolves incidents fastest lives in your SREs’ heads: “This happened in March, it was the readiness probe, the fix was a longer grace period”.

In this technical webinar, Komodor’s engineering team unveils Klaudia Memory – the long-term operational memory behind Klaudia, our AI SRE investigator. We’ll go deep into how it works: why we modeled it on human memory research (episodic vs. semantic, consolidation, reinforcement, decay), why off-the-shelf memory frameworks like Mem0 and Graphiti fell short for SRE workloads, and how we built a fast, purpose-built alternative on PostgreSQL and pgvector – one table, three search indexes, and a hybrid retrieval engine that fuses exact, lexical, and semantic search into a single ranked answer.

We’ll share real production data from three months of live operation – nearly 200K memories across 122 accounts and 1,800+ clusters, 260K+ retrievals into live investigations – and walk through real investigation patterns where Klaudia turned a multi-step derivation into a one-line recall.