Enterprise IT leaders are rightly excited about AI’s potential to streamline incident response, accelerate root-cause analysis and ease the burden on overstretched IT teams. However, one persistent misconception continues to blur the path forward: The idea that large language models (LLMs), used alone, are sufficient for solving operational problems inside complex infrastructure environments.
They’re not.
After decades of building and scaling infrastructure systems, I’ve seen firsthand that the leap from chatbot to AI agent is not just about adding automation — it’s about architectural transformation. It’s about embedding reasoning and action in the context of how real enterprises operate.
Here’s what the LLM hype gets wrong — and what it takes to build agents enterprises can trust.
It’s Not About More Data — It’s About the Right Data
Enterprises generate an overwhelming volume of telemetry: The combined output of monitoring systems, application logs, infrastructure metrics, network traces and configuration states. The problem isn’t having too little or too much data. It’s making sense of it.
A full 22% of organizations generate over one terabyte of log data daily; that’s just one slice of the puzzle. When something goes wrong — latency spikes in your checkout service — the answer isn’t buried in all your logs. It’s hidden in a few key pieces of data, scattered across different tools.
An effective AI agent doesn’t just take in everything and hope for the best. It needs to know what data to look for, where to find it and how to separate what matters from the noise. It requires smart orchestration.
Governance isn’t Optional; it’s the Starting Line
Enterprise systems live under strict access controls, audit requirements and compliance frameworks. Any AI solution entering this domain must respect those constraints. This involves inheriting role-based permissions, preventing unauthorized data movement, ensuring controlled and secure data access and verifying that outputs are free of hallucinations, particularly in mission-critical environments where sensitive data is involved.
Trust starts with respecting governance and building that into the agent from day one.
Reasoning Like an Engineer Requires Domain Knowledge
Diagnosing infrastructure issues isn’t a linear process. It’s a cycle of asking questions, checking data, spotting clues, identifying what’s missing and refining hypotheses — over and over again until the real cause emerges.
This is how skilled engineers approach complex systems. They don’t stop at the first plausible answer — they follow threads, test assumptions and dig deeper until they land on something actionable.
AI agents need to operate the same way. They must reason step by step, spot context gaps and adjust their understanding as new information surfaces. Without that kind of iterative thinking, they’re just guessing.
But reasoning alone isn’t enough. To think like an engineer, an agent needs domain-specific expertise. Most LLMs are trained as generalists. They’re good with language but don’t intuitively know when to check a DNS config, correlate alerts with a deployment, or catch a subtle memory leak.
That kind of judgment comes from embedded expertise — captured through dynamic, domain-specific runbooks shaped by years of hands-on work in production environments. It’s what transforms AI from a chatbot into a trusted teammate.
Systems are Fragmented — Agents Need to Connect the Dots
Enterprise teams rely on numerous tools for monitoring, tracing, deployment, ticketing and more. The data needed to solve a problem is rarely in one place.
According to Gartner, many enterprises use between 10 and 20 observability tools simultaneously. This proliferation of tools underscores the fragmented nature of enterprise observability setups.
A helpful agent must access information securely and reliably. That means integrating with the tools and workflows teams already use, not forcing them to rip and replace. Without this kind of interoperability, AI becomes just another silo.
What it all Means for the Enterprises
There’s no denying that LLMs are powerful. But in enterprise IT, power alone doesn’t translate to solutions. What’s needed is precision, security, adaptability, and a way to apply that power in context. That’s where enterprise agents come in.
A true enterprise AI agent isn’t just a system that can generate answers — it’s one that can take action and carry a task to completion autonomously, just like a trusted engineer would.
For enterprises looking to integrate AI into their operations, the question shouldn’t be, “Can an LLM do it?” It should be, “Can I trust this system to reason, troubleshoot and act like part of my team?”
If the answer is yes, you’re on the right track.
