Operations | Monitoring | ITSM | DevOps | Cloud

Earlier this year, Datadog for Government achieved FedRAMP High certification, extending our GovCloud environment (US1-FED) to the federal government’s most sensitive civilian workloads. That certification now covers Datadog Incident Response, bringing paging, incident coordination, automation, and postmortem workflows into US1-FED. When a government system goes down, responders need to reach the right people, coordinate a fix, and keep stakeholders informed.

Adding or renaming a product event has traditionally meant creating an engineering ticket. Datadog Product Analytics uses the same SDKs and configuration as Real User Monitoring (RUM), and those SDKs autocapture actions such as clicks and taps. But an automatically generated action name describes the element rather than the user intent behind it.

AI agent infrastructure is the set of platforms that run agents and the code they write. It has three layers: sandboxes that isolate untrusted, model-generated code, runtimes that run the agent and its services in production, and orchestration layers that save an agent’s progress so a long run can resume after a failure. Most production agents need more than one layer. This guide compares 10 platforms across all three, with isolation, state, deployment, and compliance for each.

Isolation keeps an agent’s code away from your infrastructure. A smart sandbox also lets the agent into the systems where real bugs live, without handing it the keys.

If you're using agents and MCPs to get a better understanding of your environment or work through an investigation, you can get a lot of useful information back. You can pull logs, look at recent changes, and check how services are configured, but you're still the one deciding what to do with all of it. That part of the process still lives in your head. To see where Jev might fit, look at decisions your team already makes and work backwards from them.

To monitor a database backup, make the backup script check its own output (exit code, file size, a table you know must be there) and ping a heartbeat URL only when all of it passed. If that ping does not arrive on schedule, you get an alert. A backup that failed, wrote an empty file, hung, or never started all look the same from the outside: no success ping.

To monitor Celery beat, give each periodic task its own heartbeat URL and ping it from the worker when the task succeeds, with a task_success signal handler. If beat is down, the message sits in a queue no worker reads, or the task raises, the ping does not arrive and you get an alert. The trap is that beat only publishes messages: its log prints Sending due task on schedule whether or not anything ever runs the task.

Ever wondered what is actually using your network when it suddenly slows down? It could be a device sending too much data, a backup running at the wrong time, or an application taking too long to respond. Without visibility into network traffic, finding the real cause can take hours. That’s where network sniffing tools can help. They let you inspect network traffic, capture packets, and see what is happening between devices, servers, and applications.