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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.

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.

When a branch office can’t reach the ERP system and customer calls start dropping, who gets the call? It’s almost always the network administrator. What that person does the rest of the week is less clear, and a CIO, a CFO and the admin themselves would probably each describe it differently. Part of the reason is how much the network has grown. Ten years ago it was mostly switches, cables and one building.

Coroot's node-agent has had an eBPF CPU profiler for a long time. When something burns CPU, you open the flame graph and see which function. When something waits for the disk, you have nothing: the CPU profile is flat, because waiting does not consume CPU. The metrics for disk problems are always the same. Latency on a volume goes up, utilization hits 100%, queries slow down. Every chart agrees the disk is the bottleneck, none of them says what the database was doing while it waited. Flushing pages?

Traditional monitoring watches four things: whether the system is up (availability), how fast it responds (latency), how often it errors (error rate), and how hard the underlying resources are working (resource utilization). This system works because software is deterministic: the same input produces the same output, so a deviation means something broke, and it announces itself as a timeout.

A voice agent can return the right words and still deliver a poor experience. It might leave a caller in silence, speak over them, or stop midway through a response. A transcript may look correct even when the audio the user heard was delayed, incomplete, or different from the text on screen. These problems are difficult to diagnose from text traces alone. Voice conversations also involve more than a single request and response.