Operations | Monitoring | ITSM | DevOps | Cloud

Human approval is easy when you are sitting in front of the agent. For an agent running by itself in a cluster, almost none of that holds. You’re in a meeting and your agent is running in a cluster. It has a service account, it has been asked to keep a service healthy, and it has just worked out that the right fix is to roll back a database migration. Nobody is watching it. That was rather the point of deploying it. You want to get notified to approve such an important action.

When I ask an AI agent to change code, I also want it to run the application and test what it changed. Asking it to write some tests is a start. But if it invents the expected responses from the same assumptions it used to write the code, those tests can miss the same mistake. Traffic replay gives the agent something concrete to test against: requests and responses captured from a working application.

PagerDuty’s new intelligent Model Context Protocol (MCP) tools, recently announced in Early Access, are a step toward autonomous operations. Here’s how they help customers get there.

A platform engineering team can design robust Golden Paths for agent use yet still be unprepared for what happens after adoption. An agent may authenticate properly, call the correct tools, adhere to approval gates, and complete tasks without incident, but new operational risks arise once multiple teams begin running agents continuously and in parallel. We’ve encountered these risks firsthand at Datadog.

Distributed AI training workloads impose complex scheduling requirements that Kubernetes’s built-in scheduler can’t meet. Kubernetes schedules pods individually and independently, but distributed training introduces two requirements that break this model: Pods must land on hardware with the right inter-GPU bandwidth, and all pods must be scheduled simultaneously. If either requirement goes unmet, training stalls or runs far below the hardware’s potential.

AI coding tools such as Cursor are becoming a significant source of engineering spend. But Cursor costs can be difficult for FinOps teams to manage. Cursor’s usage data alone doesn’t tell you how costs break down across users and models, and fixed-threshold alerts may not catch an unusual cost spike if spend remains below the threshold. Datadog Cloud Cost Management (CCM) brings Cursor costs into the same place where you monitor cloud, SaaS, and other AI spend.