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

A Framework for Agentic Readiness

Enterprise leaders want agents that work out of the box. What they forget is that humans don’t work that way either.

You don’t hire someone and expect them to navigate your systems, understand your policies, and make decisions on Day 1. You train them. You give them guardrails. You limit their scope until they build trust. Agentic AI needs the same foundation.

Agentic readiness has four dimensions.

1. Structured Knowledge

Agents need to know what content exists, what it means, and how it is organized. This includes:

• Taxonomies and controlled vocabularies
• Content models that define attributes, types, and relationships
• Metadata standards across systems

Without this structure, the agent can’t reason or retrieve.

2. Contextual Integration

Agentic systems must operate within a business context, not a vacuum. This means:

• Tying content to workflows, processes, and task triggers
• Understanding user roles and permissions
• Mapping intent to steps to actions

This is where IA meets business process modeling. It is the connective tissue between knowledge and behavior.

3. Interaction Design

Most AI projects fail not in the back end, but in the handoff to the user. Successful agents are designed around:

• Clear user expectations
• Intuitive escalation paths
• Transparent confidence indicators

Agents must not only act, but they must also know when to pause, ask for help, or hand off to a human. An agent must also enable real-time feedback and comments.

4. Governance and Guardrails

As with human employees, agents require oversight:

• Who decides what an agent is allowed to do?
• At what point does it require human approval?
• How is feedback collected and used to improve the model?

Why, you may ask, do we need guardrails for agents if we don’t have them for people? The answer is that organizations absolutely do have guardrails for people. Not every person in your company can approve a budget or make a legal decision. Agents need the same role-based constraints. This framework does not eliminate complexity. But it provides a structure for evaluating risk, designing responsibly, and aligning your AI efforts with your business goals.

Just as no serious organization would deploy a new system without a security or compliance review, no agent should be deployed without passing a readiness check against these four dimensions.

Lessons From the Field: What Works

After 3 decades, we’ve seen what separates the AI winners from the noise. It is not the technology or the talent behind deployment It is application of a structured process to tell the agent what content is relevant to a person based on what they ask, when they ask it and who they are.

The difference is in the groundwork that leads to the architecture of personalization. Organizations that succeed with agentic AI do not start by building agents. They start by building the knowledge foundation those agents need to operate.

We have seen this play out in companies large and small and across industries. The successful ones share a few key traits:

1. They treat taxonomy as infrastructure.

One retail organization reduced support costs by 30%. Not by building a better chatbot, but by restructuring its product taxonomy. The AI agent was the front end. The taxonomy was what made it smart. Teams that succeed invest in taxonomy early, maintain it consistently, and tie it directly to business outcomes. It is not a side project. It is the platform.

2. They design for human logic, then translate to machine logic.

A B2B manufacturer approached agent design the same way it onboards new employees. It mapped out what a person would need to complete a task—what the inputs, decisions, validations, and handoffs were. It then translated that flow into an agentic model. By mirroring human cognitive paths, it created systems that were not only more accurate, but easier to govern and explain.

3. They supervise agents like junior team members.

The most pragmatic teams view LLMs as interns. They do not expect them to know everything. They do not let them operate unsupervised. Instead, they use “confidence gates,” checkpoints where the agent must flag uncertainty and escalate to a human reviewer. This model does not slow things down. It builds trust and accelerates adoption.

4. They build content pipelines before building agents.

One global distributor realized, mid-project, that 80% of its content was outdated, misclassified, or duplicative. It hit pause, built a content enrichment pipeline, and restructured its knowledgebase. Only after that cleanup did it deploy the agent and saw double the accuracy with half the post-processing.

None of these are flashy. But they work. They reflect a maturity that goes beyond experimentation and into operationalization.

Agentic AI is not about what the system can generate, it is about what it can understand. And understanding begins with structure, context, and accountability.

Neither is agentic AI about replacing people. It is about extending knowledge. And knowledge must be curated, structured, and stewarded.