The artificial intelligence landscape is experiencing what can only be described as an identity crisis. As Bob Violino recently reported, companies are spending millions on “agentic AI” without a clear understanding of what they’re purchasing. This confusion creates a dangerous disconnect between strategic investment and actual capability.

The Great Rebranding Rush

As EY’s Dan Diasio aptly observed, “we’ve seen an incredible rebranding of anything related to generative AI presented as ‘agentic AI.'” This phenomenon has created a market where the term “agentic” has become so diluted that it’s lost its technical meaning entirely.

I previously outlined a similar problem with “agent washing,” where companies label systems as more intelligent than they are. The difference with agentic AI is that now everything is being branded with the term as a marketing push.

The confusion reaches even into established research institutions. Gartner‘s 2025 Hype Cycle for Generative AI includes agentic AI as a category, a fundamental misclassification that exemplifies the broader market confusion. Of course, they also have AGI on this chart, which doesn’t belong, and further confuses that discourse.

Agentic AI isn’t Generative AI.

These are entirely different technological paradigms serving distinct purposes.

During my recent appearance on Episode 28 of “Your Customer, Your Success” with Gary Marra, I emphasized that agentic AI is essentially a toolbox of actions that systems can utilize. It’s about providing the interface layer that enables meaningful interactions between systems and users.

• Watch the full episode here: https://lnkd.in/eAvN98Et
• Or listen on your favorite podcast platform: https://lnkd.in/esTS4YRT

Defining True Agentic AI: The Interface Layer

To cut through the marketing noise, we need precise definitions. Agentic AI is the API or the interface mechanism that sits between reasoning systems and data sources, enabling intelligent interactions between systems. This is one of the reasons I developed AgenticAPI: to define an action-based taxonomy for agentic systems further.

Consider this workflow:

Input (LLM) → Agent (reason, decision making) → Summarize (agentic) → Document (data) → Output (LLM)

In this example, “summarize” represents the agentic component—the interface that bridges the gap between the agent’s decision and the document system. What sits on either side of that interface layer is not agentic.

For more complex workflows, multiple agentic touchpoints create a chain of intelligent interfaces:

Input (LLM) → Agent (reason, decision making) → Summarize (agentic) → Document (data) → Check (agentic) → Calendar (data) → Book (agentic) → Flight (data) → Output (LLM)

Each agentic component serves as a specialized interface (summarizing documents, checking calendars, and booking flights). At the same time, the reasoning occurs in the agent layer, and the data storage takes place in dedicated systems.

Additional Workflow Examples

Note, only the blue elements are agentic which may touch AI Agents, AI Systems, Non-Agent Systems, and Non-AI Systems.

The Agent vs. Agentic Distinction

Adding to the confusion is the conflation of AI agents with agentic AI systems. These concepts, while related, are not synonymous:

• AI agents may use agentic AI systems, but they don’t require them
• AI agents are not a prerequisite for calling a system “agentic”
• Non-agent systems can leverage agentic AI capabilities
• Non-AI systems can even utilize agentic frameworks

The overlap occurs in areas like context, decomposition, and orchestration where these capabilities can exist on either side of the interface. An agent might provide context for decision-making, while the agentic system handles orchestration of the actual interface interactions.

The Three-Question Agentic Validation Framework

To help organizations determine whether their systems truly qualify as agentic, I propose a simple three-question validation framework:

1. Does the system function as an interface layer between reasoning and data systems?

True agentic systems don’t generate content or make decisions—they serve as intelligent bridges that translate between different systems, APIs, databases, and services. If your system is doing the reasoning or storing the data, it’s not the agentic component.

2. Can the system translate and interact across diverse system boundaries?

Agentic AI is defined by its ability to interface with multiple, disparate systems. A truly agentic component should be able to translate requests into appropriate API calls, database queries, or service interactions across different platforms and formats.

3. Does the system operate as a specialized connector rather than a decision maker?

Agentic systems are the “how,” not the “what” or “why.” They execute the interface interactions that agents or other systems determine are necessary, but they don’t make strategic decisions about what should be done.

If a system can’t answer “yes” to all three questions, it’s likely a reasoning system, data store, or AI assistant being marketed as agentic AI.

The Cost of Confusion

The market’s definitional chaos isn’t just academic. EY’s survey reveals that 21% of senior leaders have invested $10 million or more in AI, with a third planning similar expenditures next year. Yet only 14% report full implementation of agentic AI technology in their organizations. Of these implementations, most are not actually agentic.

This gap suggests that companies are purchasing solutions that don’t match their expectations or needs. When executives believe they’re purchasing agentic capabilities but instead receive sophisticated chatbots or reasoning systems, the resulting disappointment often leads to AI skepticism and reduced future investment.

Understanding the Interface Economy

What makes this distinction crucial is that agentic AI represents a fundamental shift in how we think about system integration. Traditional APIs require developers to understand specific protocols, authentication methods, and data formats for each service they want to integrate.

Agentic AI creates intelligent interface layers that can dynamically adapt to different systems, translating between formats and protocols as needed. This is automation, not autonomy. It’s automation that understands context and can adjust its interface behavior based on the systems it connects to.

If done right, this shift unlocks enormous value, growth, and revenue potential. To put it in perspective, generative AI alone could add between $2.6 trillion and $4.4 trillion in economic value annually across use cases like marketing, sales, customer operations, software engineering, and R&D. Industries most exposed to AI are already seeing triple the growth in revenue per employee compared to less exposed sectors. If you treat agentic AI as a precise, powerful component (rather than hype), you position yourself to capture a disproportionate share of that upside.

Building Genuine Agentic Capabilities

For organizations serious about implementing true agentic AI, focus should be on identifying where intelligent interface layers add value. This means:

• Mapping current system integration challenges
• Identifying repetitive interface tasks that require context awareness
• Understanding where translation between different data formats and protocols creates bottlenecks
• Recognizing opportunities for adaptive interface behavior based on system context

These efforts must be grounded in realistic expectations about what agentic AI actually provides. It’s not artificial general intelligence, decision-making capability, or data storage. Agentic AI is sophisticated interface automation that can adapt to context.

A Legitimate Question: When Does a Component Define the Whole?

One question I keep returning to in the current wave of “Agentic AI” hype is this: if a workflow includes an agentic component, does that make the entire process agentic by default?

I see many labeling systems or workflows as “agentic” when, in reality, they only include agentic capabilities as part of a broader architecture. This raises an important point about how we define and name complex technological systems.

We don’t redefine entire workflows based on a single component. A login process may involve multiple APIs, yet we still refer to it as a login process, not an “API process.” An e-commerce checkout flow may involve database queries, payment gateways, and inventory checks, but its primary function is to name it. The same logic applies to other components:

• A platform built with microservices isn’t called a “microservice system.”
• A workflow that uses a load balancer isn’t a “load balancer workflow.”
• Systems using Kafka or RabbitMQ aren’t “message queue processes.”
• A site using a CDN isn’t a “CDN application.”

Each of these serves a technical purpose within a larger system, but none defines the whole. Agentic AI should be treated the same way: as a powerful component that enables intelligent interfaces, not the defining characteristic of an entire workflow.

The confusion likely comes from the technology’s novelty and the rush to associate with the latest trend. But broadening the definition too far risks stripping the term of its meaning. Precision matters. We should describe systems based on their core function and purpose, while recognizing that agentic AI is one layer within the architecture, not the architecture itself.

The Path Forward

The agentic AI market needs a reality check grounded in technical precision. As Deepankar Mathur from Searce notes, organizations feel “perpetually behind the curve” in this rapidly evolving landscape. The solution isn’t to chase every new trend labeled “agentic,” but to understand that agentic AI serves a specific, crucial function as intelligent interface infrastructure.

Actual progress requires moving beyond the hype cycle toward practical implementation of well-defined interface capabilities. Companies should focus on identifying specific integration challenges that would benefit from intelligent, context-aware interface layers, and then evaluate potential solutions against clear technical criteria rather than relying on marketing buzzwords.

The agentic AI revolution is real, but it won’t be built on rebranded chatbots, reasoning systems, or databases labeled as “agentic.” It will emerge from organizations that understand the distinction between making decisions and interfacing between systems, and invest accordingly.

Only by clarifying these definitions can we move from an era of expensive confusion to one of meaningful agentic capability deployment. The future of intelligent system integration depends on getting this right.

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Chris Hood is an AI strategist and author of the #1 Amazon Best Seller “Infailible” and “Customer Transformation,” and has been recognized as one of the Top 40 Global Gurus for Customer Experience.