Operations | Monitoring | ITSM | DevOps | Cloud

When Datadog acquires a company, we usually rebuild the product rather than plugging it in as is. That’s exactly what we did with Eppo, an experimentation and feature-management platform. Eppo’s feature-management capabilities became Datadog Feature Flags, while experimentation became Datadog Experiments. This post focuses on the experimentation platform and four changes we made to help you get to a decision faster.

AI agents can help investigate failed builds, fix flaky tests and automate other development tasks. But setting up those agents has required more Pipelines configuration than it should. Agent-powered steps often need different compute, permissions and runtime settings from ordinary build and test steps. Until now, teams have either repeated those settings across every agent-powered step or tried to make one set of global defaults work for everything.

The best API monitoring tools are Hyperping for HTTP and API checks with on-call and status pages, Checkly for API monitoring as code, Postman Monitors for teams that already keep collections in Postman, Datadog for connecting failed checks to traces and logs, Grafana Cloud for teams using k6, Better Stack for checks inside a broader incident workflow, and UptimeRobot for inexpensive availability checks.

Somewhere in every compliance program is a person who spends the week before an audit pulling logs out of several different systems, reconstructing who had access to what, and hoping the screenshots match what the auditor actually asks for. None of this work makes the system more secure. It just makes the existing security visible to someone who's checking. That gap, between the controls that are actually in place and the evidence that proves it, is where most audit prep time goes.

We’re back with another major SIGNL4 update! This release brings new features and improvements across Call Routing, User Profiles and Management, Stakeholder Communication, and AI.

The State of Agent DLC 2026 asked 700 organizations already running AI agents how confident they were across five domains: testing, security, inventory, cost, and rollback. Confidence came back between 74% and 77% in every domain we tested. In most of them, the controls that would justify it are not there. “No, I don't have a nanny cam, but I'm sure my kids are OK.

Ask a platform team why their application logs aren't in their observability backend and you'll often get a one-sentence answer: And, that's where the conversation ends. The logs stay in a silo. Or, they don't get collected at all. The team loses the troubleshooting signal, and nobody revisits the decision because the alternative looks like a compliance violation. Application logs in healthcare, aviation, insurance, and retail are full of personal information that should not be stored in plain text.

Learn why modern IT complexity can slow incident response and how observability, AIOps, operational context, and automation can help teams detect, investigate, and resolve issues faster.

Certification against ISO 20000 puts your service desk under audit. That audit runs on what your team wrote down at the time. The standard does not care how your team describes its process. It does not care which ITIL 4 practices you adopted. It cares what your records show, so auditors spend their time in your tickets, approvals, and review minutes. In this blog, you will: You will finish knowing which of your records would survive an audit.

Most log management tools bill you on log ingestion, the volume of data you send them. That works until your log volume doubles, and the invoice doubles with it. The best log management tools let you control what gets indexed and kept, so growth stops being a budget problem. In this blog, you will see: By the end you will know which fits your volume. Log management is the full lifecycle of your log data, from the moment it is collected to the moment it is deleted.