If you can see everything, you may see nothing at all. That’s what SREs and DevOps engineers are learning as observability tools multiply and human capabilities do not.

Engineers have never had more observability data than they do today, affording them unparalleled visibility into the systems they manage. But all that information at their fingertips doesn’t always equate to faster detection, let alone resolution.

Why not? Problematic observability data lighting up a dashboard merely sends a human into collected logs and traces to dig up the cause of the problem. Suddenly, the collected information becomes both a boon and a headache. The required context is there for you to find and use, but it’s difficult to locate.

Worse, you may chase the wrong lead, leading to exploration of false paths, wasted time, and perhaps even extended downtime. If there’s too much data, why not throw more engineers into the breach? Multiple engineers looking into the same problem can quickly metastasize into a multi-platform coordination nightmare, leading to unpredictable resolution timelines.

To fully capitalize on the modern observability stack and all the information it collects, a new technique is needed. Instead of humans hunting and pecking through logs, the modern enterprise wants a single, unified system that can parse observability data quickly and either execute a fix itself or suggest a mediation pathway for humans to manage.

Yes, we’re talking about AI. Specifically, AI agents. Agents are a strong fit for the observability crisis, as they can handle higher data volumes than humans — especially high-volume data split across different systems that require correlation — and have recently gained the ability to act autonomously. 

The good news is that technology companies are building that precise system. Even better news is that the same tools allow engineers to directly bring observability data into the agentic development environment of their choice, such as Codex, Cursor, and Claude Code, so they can unite what they know about an issue with the tools they need to resolve it. 

At 12 p.m. Eastern/9 a.m. Pacific on Tuesday, June 30, Datadog’s Vignesh Palaniappan, Senior Product Manager for Bits AI, joins The New Stack to discuss the pain points you’re experiencing with observability today, how AI agents offer a solution, and how to set your engineering team up with the tools it needs to operationalize the information it has at its disposal.

Register now

What you’ll learn

• How to quickly surface root causes behind alerts without sending your engineers off on a wild bug hunt. Who doesn’t want shorter MTTD?
• How to build and deploy agents that can remediate alerts and issues on their own. Who doesn’t want shorter MTTR?
• How to import observability data directly into tools like Codex, Cursor, and Claude Code so your developers have the context they need right where they work.

Can’t join us live? Register anyway, and we’ll send you a recording after the session. By registering, you consent to receiving email communications from The New Stack and Datadog.

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