Operations | Monitoring | ITSM | DevOps | Cloud

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.

Give a model permission to act on your computer and you're trusting it will behave the way you expect. It might not. In this clip from our Braintrust conversation, Adam Berman, engineering leader at Semgrep, breaks down why a backdoored model is a different threat model than backdoored software. You can't fuzz-test your way to finding it, and you often can't detect it until it's already acting the way it shouldn't.

Matt Zytaruk, VP of Technology at Minga, on what changed once his developers could point their AI tools at their own infrastructure. Minga checks in 1.5 million students across the eastern seaboard every school morning. They run that platform on Control Plane, which gives their team an MCP server, a CLI, and Terraform-native resources — so a developer can investigate an issue and generate the code for the change without waiting on anyone.

Average throughput across all projects on CircleCI increased 59% YoY.

Password resets are a perfect starting point for agentic automation. See how Resolve Agent Lab builds an AI agent that verifies users, resets credentials, and closes the loop automatically.