The Agentic Spectrum: Where AI Agents End and Agentic AI Begins

I read somewhere that the dawn of the AI agent was “one of the defining stories of 2025,” a sign that we’re moving beyond mere analytics to systems that have the power to act. AI agents have seemingly replaced generative AI as the hot new thing in AI, as evidenced by the proliferation of AI agent solutions and numerous articles about them. Then, there’s the term “agentic AI.”

Engineering.com recently reported that DHL is carrying out the “next phase” of its AI strategy, which “focuses on agentic AI for its contract logistics division.” Elsewhere in the article it says that DHL Supply Chain is interested in integrating AI agents “to drive greater process efficiency for customers while making operational roles [better] for employees…” 

Apparently, the logistics giant has already deployed HappyRobot’s AI agents or “AI workers” across several use cases, including appointment scheduling, driver follow-up calls, and high-priority warehouse coordination; and internal teams continue working on designing new agentic capabilities. 

CONFLICTING TERMS

“AI agent” and “agentic AI” are often used interchangeably, but it’s not clear they are the same. When it comes to AI agents, in particular, sources disagree on level of complexity, degree of autonomy, and ability to learn. Some describe AI agents as little more than basic chatbots – designed to perform specific tasks within defined parameters – while others assign them more advanced capabilities like the ability to plan and use/integrate with tools like APIs, databases and search engines.

Agentic AI, on the other hand, could refer to a system of multiple AI agents. Some describe agentic AI as the next generation of intelligent systems capable of coordinating multiple AI agents to solve complex problems. Agentic AI might refer to a broader category and AI agents to actual agentic AI systems. Or, perhaps it’s more like a spectrum and AI agents are part of a broader evolution towards agentic AI.

And what about AI assistants? There are AI assistants now on websites and AI “copilots” in software. There are AI agents and AI assistants for training, some taking the form of virtual humans or avatars. Are AI agents just AI assistants rebranded, or are AI agents more advanced than assistants?

POWER TO ACT

The crux of the matter seems to be autonomy: At one end of the spectrum, you have basic AI agents or assistants adhering to established rules. At the other, more futuristic and ambitious end, you have advanced multi-agent systems or networks of proactive, independent and adaptive AI agents working together to achieve complex objectives. Agentic is thus both a descriptor and a goalpost. 

Perhaps it’s more accurate to say that the definitions of and relationship between AI agent and agentic AI are still evolving. The “trend” is moving towards agentic AI, from traditional scripted chatbots to agent-assist systems that index and retrieve knowledge (where we are today) and ultimately to agentic AI that understands context and acts on it with minimal to no human oversight. 

MOVING TOWARDS AGENTIC AI

Let’s go with: An AI agent is a software system or application that uses AI to complete tasks on behalf of users. Instead of simply responding to prompts, AI agents can plan multistep tasks and use tools independently. They can be autonomous (still require initial human setup) or semi-autonomous (require a human trigger like a customer query).

A basic example would be a virtual assistant that responds to voice commands. Siri, for instance, can set a reminder for you, even consult the Internet or open an app, but cannot, say, plan a trip to Paris. Self-driving cars and enterprise automation agents like Aisera capable of IT, HR, customer service and finance automation are examples of more advanced AI agents. 

At their most advanced, AI agents with large language models at their core can process and analyze data, recognize patterns, create subtasks, make decisions, and execute diverse actions such as scheduling appointments, processing refunds, debugging code, or generating content to perform complex, multistep workflows. These agents are capable of reasoning, using tools like external APIs, software and other machine learning models, and learning/improving over time based on new experiences and data. This is where “agent” and “agentic” start to overlap. 

CURRENT STATE

It’s currently impossible to strictly differentiate between AI agent and agentic AI because there is no general consensus. Agentic AI is certainly an exciting field of AI with profound implications for the future of work, but is it truly here yet? While specialized, goal-oriented AI systems exist, perhaps fully realized agentic AI is on the horizon. 

We’re not yet in the era of fully “hands-off” multi-agent stacks. For one, the more agents at work today, the more expensive and unreliable. Agents can compete over resources, and human-in-the-loop (HITL) oversight remains crucial for complex, high-risk, and open-ended tasks as well as exceptional (edge) cases and ethical considerations. Validation, monitoring and regression testing are essential, not optional safeguards.

Agentic AI is (probably) coming, but not until multi-agent systems are affordable, reliable, and governable at scale.