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[WEBINAR] Codifying Tribal Knowledge - How to Build Long Term Memory for AI SRE Agents

In this technical webinar, Komodor’s engineering team unveils Agent Memory – the long-term operational memory behind our AI SRE agents. 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.

Building AI SRE Agents, Part 2: Leave the Laptop, Earn Trust

Moving the agent off your machine and pointing it at real clusters — read-only, in shadow mode — then climbing a trust ladder toward carefully scoped action. This is the second article in a three-part series on taking an AI SRE agent from a weekend experiment to enterprise production. Part 1 built a local agent on a throwaway cluster: read-only, propose-only, refined against a small eval set, with portable skills and no production write access.