Operations | Monitoring | ITSM | DevOps | Cloud

We gave five metrics backends the same budget, the same week of data and the same ten queries, then ran those queries at rising concurrency. At one query at a time, the open source engines looked strong. Under concurrency, two of them stopped answering, and the rest slowed down by an order of magnitude.

Configuration Management Databases (CMDBs) have long played an important role in IT Service Management, providing a structured record of technology assets, their configurations and relationships. For enterprises managing complex IT environments, they remain a valuable foundation for understanding infrastructure and supporting operational processes.

For the last few weeks I’ve used a coding agent to find and fix backend performance issues in Sentry, starting from the traces Sentry sends to itself. Most of the fixes only changed a line or two. But together, they removed hundreds of millions of queries a week, and some expensive lookups became hundreds or thousands of times faster (more details on that below).

Teams that build agents on open models hosted on Fireworks AI, such as GPT-OSS, Kimi, GLM, Qwen, and MiniMax, need a reliable way to measure whether their LLM applications and agents are giving accurate, safe, and helpful answers efficiently. Datadog Agent Observability now supports Fireworks AI as a model provider, so you can get end-to-end visibility into your agents built on Fireworks AI, from traces and token usage to quality evaluations.

AI can already tell operations teams what is wrong. The harder part is everything between knowing and doing. Skylar is closing that gap through guided action: recommendations grounded in live operational context, reviewed by the right owner, and connected to governed execution. In many post-incident reviews, the surprising moment is not when the team finally understood the problem. It is when they realize how long the organization waited after it already knew enough to act.

Running multiple AI coding agents in parallel across repositories sounds like a productivity multiplier. In practice, it creates a coordination problem: conflicting file edits, orphaned branches, and zero visibility into which agent finished what. AI coding agent orchestration tools exist to solve exactly this, and Kepler by GitKraken gives you one surface to direct every agent from backlog to merged pull request. This guide compares seven tools built for multi-repo, multi-agent development workflows.

The best alert is the one that never fires. Picture an idyllic Tuesday afternoon: last night's release nudged checkout p99 up 41%, but an agent noticed. Nobody got paged, and your SLOs didn't start burning. Instead, the agent acted and already narrowed it down to one retry change. It filed a ticket for the Payments team and flagged the PR. By the time anyone would have noticed, the fix is in review. That's what SRE was always supposed to feel like.

Domain-specific ITOps agents are designed around the systems, data, and tasks involved in operating production IT. Learn how specialized agents work together and how Edwin AI applies them across incident investigation and response.