Before We Begin: Here’s Why Definitions Vary

The phrase “agentic AI” sits on a spectrum with no shared boundaries, which helps explain why definitions differ so dramatically. A major driver of this variation is the disconnect between how academics use the term and how it’s marketed commercially.

At the academic end of the spectrum, “agentic” refers to systems that can understand a goal, break it into steps, make decisions along the way, and adjust their plan as new information comes in. 

At the commercial end of the spectrum, however, agentic AI is often used to label far simpler tools. This includes basic chatbots and prompt-driven workflows that perform a predefined action but don’t demonstrate meaningful planning or showcase autonomy. 

Both ends of the spectrum get grouped under the same term, and it’s one reason why Gartner placed agentic AI at the “peak of inflated expectations” in its 2025 Hype Cycle for Emerging Technologies. The term is everywhere, but the capabilities it refers to are far from consistent.

AI Agents vs. Agentic Workflows: What’s the Difference?

Further complicating matters, there’s an important distinction to be made between an AI agent and a larger agentic workflow. 

An AI agent is usually a single system built to handle one specific job. It might rely on a large language model or call in a few tools (like an API or a browser). It might also react to real-time inputs and work through a step-by-step sequence to complete a task. 

Agentic workflows take things a step further. Instead of relying on one agent to do everything, they coordinate multiple agents. Each agent has a specific role, and they all work toward the same goal. One agent might plan while another gathers information and a third takes action. Agentic workflows can also adjust course as things change, adapting to the nature of work rather than rigidly adhering to a defined sequence of events.

With this distinction in mind, let’s take a look at how leading voices in AI are using the terms.

What Do the Leading Voices Mean When They Say “Agentic”?

Major research labs, leading firms, and market analysts describe agentic AI in a variety of ways, but most cluster around a few core ideas:

• OpenAI emphasizes that agentic AI is a system “that can pursue complex goals with limited direct supervision.” This means the technology can take actions, use tools, and complete tasks with minimal human intervention. 
• Google DeepMind defines agentic AI as “an advanced form of artificial intelligence focused on autonomous decision-making and action.” Or, to put it in the words of Google’s CEO, Sundar Pichai: “Anywhere you can describe a task in natural language, [agentic AI] can act on your behalf to accomplish that.”
• Andrew Ng, a British-American computer scientist and one of the leading voices in agentic AI, defines agentic workflows as something that can “iterate over a document many times.” He says that agentic workflows can plan outlines, decide what web searches are needed to gather more information, write a first draft, and even revise the draft taking into account any weaknesses.
• Dario Amodei, CEO of Anthrophic, summarizes his definition of agentic AI as a “country of geniuses in a datacenter.” What he means is this: AI systems can be assigned complex, long-running goals and then independently plan, coordinate, and take action across digital and physical systems.
• IBM focuses on the workflow dimension. They describe agentic AI as software that “exhibits autonomy, goal-driven behavior, and adaptability” and that can execute tasks across systems and data sources, acting as a digital worker inside an enterprise.
• McKinsey and Gartner take a business-first approach. McKinsey describes agentic systems as “a software component that has the agency to act on behalf of a user or a system to perform tasks,” while Gartner leans on agentic AI’s “potential to automate interactions and processes, both for service teams and for the customers making requests.”

The common throughline between all of these definitions is this:

With this in mind, let’s unpack the common threads that sit at the core of most definitions.

The Common Threads Between These Definitions

Different organizations may use different words, but they’re describing the same behaviors. These shared capabilities offer the clearest picture of what agentic AI actually is, and they begin with one fundamental shift:

1. Agents act, not just respond.

Across definitions, one of the most consistent themes is that agentic AI takes initiative. At the risk of stating the obvious, AI agents have agency. Unlike traditional generative AI models that wait for the next prompt from a person, agentic systems are expected to take action once a task has been set. 

This shift from passive response to active behavior marks a fundamental change in how AI is set to contribute to work inside an organization.

2. Agents pursue a goal

Another shared element is goal orientation. 

Agentic AI isn’t designed to provide isolated outputs based on a single input. Instead, it’s designed to autonomously move toward an outcome. 

Whether the goal is to generate a report, reconcile data, or triage a request, the system is expected to make progress without any step-by-step handholding. This pursuit of an end state is central to how the leading voices are defining agentic capabilities.

Agentic AI isn’t designed to provide isolated outputs based on a single input. Instead, it’s designed to autonomously move toward an outcome. 

3. Agents execute multi-step plans

Nearly every definition of agentic AI points to the ability to move through work in multiple steps. What differs is how those steps are determined.

In some systems, the plan is probabilistic. For example, the agent figures out what to do next as it goes and adapts based on context. This is the model often highlighted in research discussions, where agents dynamically reason and explore paths toward an outcome. 

In many practical enterprise workflows, however, the plan is more deterministic. The steps are predefined or strongly guided by humans, policies, or business rules, and the agent’s role is to execute and adapt within the margins of what’s acceptable.

4. Agents can interact with systems on behalf of humans

Finally, agentic AI is expected to operate within real workflows. That means using tools, accessing data, triggering processes, and completing tasks that previously required human intervention. 

Of course, a human stays in the loop to guide decisions and own the outcome, but the ability to interact and cooperate with various systems is what distinguishes agentic workflows from other AI models that only generate text or provide basic recommendations.

It does this by “reasoning through the work”, so to speak. That is to say: it deploys the right tools at the right time, navigates obstacles as they come up, and loops a human in where needed to provide judgment, direction, and accountability.

Agentic AI: Turning a Definition into Forward Direction

Almost 30 years ago, the phrase “cloud computing” was little more than a vision. But a shared language gave organizations the clarity needed to experiment with it, invest in it, and eventually transform entire industries.

Agentic AI is at a similar moment in time. And by examining how leading voices define agentic AI, a clear picture emerges: Agentic AI acts rather than responds, pursues outcomes rather than prompts, executes multi-step responses, and interacts with tools and systems on a human’s behalf.

When leaders understand what this language actually means, they can better evaluate opportunities, distinguish marketing hyperbole from real capabilities, and start to make informed decisions about where agentic systems can fit within their existing workflows.

Are you ready to learn how a grounded, real-world approach to agentic AI can help your teams work smarter and deliver stronger outcomes?