#jvm-agents

#jvm-agents

As agentic adoption of GitHub CLI grows, our team needs visibility into how features are being used in practice. We use this data to prioritize our work and evaluate whether features are meeting real user needs.

Datadog recently announced that its LLM Observability platform now provides automatic instrumentation for applications built with Google's Agent Development Kit (ADK), offering deeper visibility into the behavior, performance, cost, and safety of AI-driven agentic systems. The integration, highlighted on the Google Cloud Blog, aims to make it easier for developers and SRE teams to monitor and troubleshoot complex multi-step AI agent workflows without extensive manual setup or custom instrumentation.

While the codebase is fresh and grows fast under the umbrella of the local environment, we tend to rely on debugging tools, which were created specifically for that purpose. The app is half-baked, and the code is split open. We observe it through the lens of our IDE and with the speed of our brain. Everything is possible; we may pause execution for minutes, and the whole system is a white box - an open book for us.

Just a couple of words about today's topic. Of course, nothing surprising here, AI is changing DevOps and is changing the way teams are moving beyond reactive monitoring towards predictive automated delivery and operations. What does that mean? How can teams actually implement predictive incident detection, intelligent rollout, and AI-driven remediation? Also, how can we accelerate delivery? Those are all topics that today's panelists hopefully are going to cover.

Support for distributed systems. Check how well the tool handles microservices, serverless, and Kubernetes. Can you follow a request across services, queues, and third-party APIs? Does it understand pods, nodes, clusters, and autoscaling events, or does it treat everything like a static host? Correlation across metrics, logs, and traces. In an incident, you shouldn't be copying IDs between tools. Look for the ability to pivot directly from a slow trace to relevant logs,

From the discussions in the Jakarta EE Platform call[s] the last couple of weeks, it looks like we won't see a release of Jakarta EE 12 on this side of summer (on the Northern Hemisphere at least). The reason is that since Jakarta EE 11 was delayed by a year, most of the vendors are currently working on their implementations.