Autonomous IT Operations: Why AI Agents Fail Without Unified Data
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By Amit Shingala, CEO of Motadata
Almost every conversation I have with enterprise IT leaders this year ends at the same place: AI agents. The promise is appealing, and a good part of it is real. A system that spots an anomaly, understands its cause, and fixes it without anyone having to wake up at three in the morning.
Our industry has chased that idea for a decade under different names. This time the technology finally seems to be keeping up.
And yet I think most of these projects are going to disappoint over the next two years. Not because of the agents. Because of what we are asking them to act on.
An Agent Is Only as Good as the Data Underneath It
When a company connects an agent to an infrastructure where metrics live in one tool, logs in another, traces in a third, and tickets in a fourth, it is not automating a decision. It is automating a guess.
The agent acts quickly on an incomplete picture. A fast mistake made at scale is worse than a slow one made by a person who at least hesitated.
Picture what that looks like on a normal Tuesday. A latency spike shows up in the application monitoring tool. The real cause sits in a saturated network link that a separate tool is tracking. A third system holds the log line that explains the whole thing.
An agent reading only the first signal will restart a healthy service, chase the wrong host, or close a ticket that should have stayed open. It will do that in seconds. Then it will do it again the next time the same pattern appears, because nothing in its view ever connected the three signals.
The Real Problem Is Fifteen Years of Siloed Tools
Here is the underlying problem few people want to name. The industry has spent fifteen years buying monitoring tools in layers. One for the network, one for servers, one for applications, one for logs.
Each handled its own patch well, and each spoke its own language. As long as a human engineer translated between them, the model more or less held.
The human correlated in their head what the tools never correlated with each other. That invisible work is exactly what we are now asking an agent to take over. We are handing it the same mess without the translator.
What I see most often is enterprises treating the agent as the project and the data as a detail to sort out later. The order is backwards.
Intelligence is the easy part to buy now. The hard part, the part that decides whether any of it works, is whether the signals the agent reads actually agree with each other.
Why Unified Data Has to Come First
The conclusion I draw is not that we should slow down autonomous AI. It is that order matters. You unify the operational data foundation first, then you delegate to the machine.
An organization that correlates metrics, logs, flows, and traces in a single context gives the agent something real to reason over. An organization that has not done this is asking AI to compensate for a broken architecture, and no model fixes that.
Single context is not a slogan. It means every signal shares a timeline, a topology, and a common set of identifiers. When that is true, correlation stops being guesswork.
The agent can trace a symptom back to its cause because the cause and the symptom live in the same picture. That is the line between an agent that acts and an agent that gambles.
What This Looks Like in Practice
This is the conviction we built ObserveOps on: one observability platform where those signals cross from the start, instead of stitching four products together with fragile integrations.
When that foundation connects to service management in ServiceOps, an alert is not just detected. It becomes an action carrying the full context of the affected asset.
It is the same reason our own agentic automation runs on that unified layer rather than beside it. An agent is only worth deploying once the ground beneath it agrees with itself.
I raise this not as a product catalogue but because it is the lesson that took us the longest to learn, and the one I repeat the most. Intelligence is built on unified data, not on abundant data.
The Honest Cost of Getting Your Data in Order
It is worth being honest about what this costs. Unifying the operational foundation is neither free nor fast.
For most enterprises it means rethinking investments made in good faith, retiring tools that are still being paid off, and above all bringing along teams that have spent years comfortable with their own dashboards.
It is a job of months, not a quarter. The hard part is rarely technical. It is organizational.
The network team trusts its own view. The application team trusts its own. Asking both to give up a familiar dashboard for a shared one is a harder sell than any procurement decision. Anyone promising otherwise is selling the agent before solving the ground it has to stand on.
The Question Every IT Leader Should Ask
For IT leaders in Spain mapping out their AI roadmap right now, the useful question is not which agent to buy. It is more uncomfortable and cheaper to answer.
If an autonomous system made a decision today on the data they have, would they trust it?
If the answer is no, the project does not start with AI. It starts with putting in order what the AI is going to read.
Where Autonomous IT Operations Are Heading
Autonomy in IT operations is coming, and sooner than many expect. The companies that get there first will not be the ones that buy the most sophisticated agent.
They will be the ones that did the boring work of getting their data in order while everyone else argued about models. The agent gets the headlines. The foundation decides whether the headlines hold up.
Artificial Intelligence – The Data Scientist
