Why Observability is the Missing Link in Unlocking Agentic AI’s Full Potential

Why Agentic AI Needs to Be on Your Radar

When Ashan Willy, the CEO of New Relic, took the stage at Transform 2025, he brought a fresh—and much-needed—perspective to the AI conversation. Instead of just touting data or breakthrough algorithms, Willy spotlighted something many overlook: observability. His message was simple but hard-hitting: as we build smarter, more autonomous AI systems—what he calls “agentic AI”—we need to make sure we can actually see how they work.

So what exactly is agentic AI? Think of these as systems that don’t just process data or follow basic instructions; they make decisions by themselves in real time, working toward goals we set for them. But with this kind of autonomy comes complexity. It’s a bit like hiring a super-smart employee who never sleeps and never asks for days off, but who also moves so quickly that it’s difficult to keep track of what they’re doing. That’s why Willy keeps hammering home the need for better visibility—or observability—into their actions.

Willy puts it plainly: “You can’t improve what you can’t measure.” The more independence we give these AI agents, the more important it is to be able to follow their thought processes, measure their performance, and understand their choices. Observability tools are the answer—they give development teams a real-time window into what these AI agents are doing, how they’re performing, and whether they’re behaving as expected.

Making the “Black Box” Transparent

There’s a long-running joke (and an ongoing headache) in tech about the “black box” problem. We build smart systems, but sometimes even the creators can’t explain exactly how an AI engine arrived at a particular conclusion. This isn’t just a curiosity—it’s a real issue, especially when AI is used in places where mistakes aren’t an option.

This is where Willy and his team at New Relic come in. Their goal is to change that black box into what he calls a “glass box”—where everything inside is visible, measurable, and manageable from day one. By baking observability into the very core of autonomous AI systems, they’re aiming for a world where decisions can be traced, tweaks can be made quickly, and accountability isn’t an afterthought.

The Real-World Value of Observability

Beyond transparency and trust, there’s another big reason to care about observability: return on investment. Companies are pouring serious money into AI, and it’s no longer enough to hope things will work out. Observability provides the essential data and context you need to tune performance, streamline operations, and spot trouble before it blows up.

As Willy puts it, when you can monitor what your AI agents are up to, you can iterate more rapidly, fix problems before they become disasters, and ultimately deliver a lot more value. In other words, observability is what allows teams to move from just reacting to problems to proactively managing them—making AI safer, faster, and more reliable everywhere it’s deployed.

And as more businesses roll out their own intelligent agents, the need for robust observability will only grow. Companies that make transparency and monitoring a priority now won’t just have better AI—they’ll have a real advantage, building more dependable and scalable systems for whatever comes next.

For a deeper dive into Ashan Willy’s vision, and to see how New Relic is helping shape the future of agentic AI, check out the full article on VentureBeat: here.