The Rise of Agentic AI: Why Your AI Agent Is Clueless

Enterprises are rushing to adopt so-called “agentic AI,” systems that promise to not just answer questions, but also take actions, automate tasks, and drive decisions. In theory, these agents can draft emails, update records, generate product specs, or flag anomalies without human involvement. In practice? Most deployments don’t make it past the demo.

We’ve seen this movie before. Pilots launch. Nothing goes live. The chatbot doesn’t work. And yet we keep calling it AI, when it’s actually giving us the illusion of intelligence.

We’ve talked to dozens of organizations across industries, and the pattern is depressingly familiar:

• A well-meaning team spins up a proof of concept using a large language model (LLM).
• They connect it to a content source, maybe add a prompt template.
• It seems to work ... until it doesn’t.
• Results are inconsistent. Business users don’t trust it. Adoption stalls.

The root cause isn’t model accuracy or even hallucination. It’s lack of context.

These systems may be capable of reasoning. But reasoning requires structured knowledge, and most enterprises simply haven’t done the work to provide it. Today’s language models are brilliant at generating language but not meaning.

They don’t know your business rules. They don’t understand your product catalog. They don’t know which version of the policy is current or which one is safe to use. That’s not intelligence. That’s autocomplete with attitude.

One knowledge management (KM) lead summed it up this way: “We didn’t build an agent. We built a guess engine.”

I’m not saying agentic AI is a dead end. Far from it. But until enterprises ground these systems in structured, governed, well-modeled knowledge, they’ll continue to overpromise and underdeliver.

DEFINING AGENTIC AI WITHOUT THE HYPE

The term “agentic AI” is already suffering from misuse. Like “digital transformation” and “machine learning” before it, it’s on a fast path to becoming a buzzword no one can define and everyone claims to offer.

Let’s be clear. Agentic AI refers to systems that can take autonomous action based on context and intent. That means they don’t just respond. They perform tasks, trigger workflows, retrieve information, make decisions, and learn from outcomes. They are meant to behave more like digital employees than tools. But there’s a catch. Agency requires judgment and judgment requires context.

Most of what’s being marketed as agentic AI today is really just a wrapper around a language model:

• A fancy chatbot that fills out a form
• A plugin that summarizes tickets
• A tool that emails a recommendation based on a prompt

These are helpful features, but they aren’t agents. They don’t understand your enterprise’s logic. They aren’t goal-directed. They don’t know when to stop, escalate, or check for exceptions.

As one engineer put it during a failed deployment: “Agents need to know when they are out of the correct context—they need to know the boundaries and only operate within them—just like the boundaries of what an employee can do. If that is not understood, it will not meet a higher threshold of capability. It will stay stupid.”

True agentic systems must operate within constraints. They need this type of knowledge:

• Business rules and policies
• Workflows and handoff points
• Task sequences and dependencies
• What constitutes a “good” or “safe” outcome

That is not something a foundation model brings out of the box. It happens through information architecture, contextual modeling, and structured content.

This is where many projects go sideways. Teams assume the model will figure it out. But the model is only as smart as the scaffolding around it. Autonomy is not magic. It’s architecture.

And until that architecture exists, as with consistent metadata, structured vocabularies, and well-formed content, the agent remains little more than a random content generator.