AI agents vs agentic AI: what’s the difference?

An AI agent is software that takes a task, decides the steps and carries them out using tools. IBM defines it as “a system that autonomously performs tasks by designing workflows with available tools.” AWS describes it as a program that can interact with its environment, collect data and use that data to perform self-directed tasks, with people setting the goal and the agent choosing the steps.

The tools are what make it an agent rather than a chatbot. A plain chatbot can only talk. An agent can look up a customer, check a calendar, send a draft email or call another app. IBM lists types from simple to advanced: simple reflex agents that follow fixed rules, model-based agents with memory, goal-based agents, utility-based agents that weigh trade-offs, and learning agents that improve over time.

Most AI agents run the same loop: understand the task, plan the steps, use a tool, check the result, and repeat until done. The model inside does the thinking; the tools let it act.

1. 01Take in the request and any context, such as who the customer is.
2. 02Plan: decide which steps are needed and in what order.
3. 03Act: call a tool, such as searching orders, reading a calendar or drafting a reply.
4. 04Check: look at what came back and decide whether the task is done.
5. 05Stop and report, or ask a human when a rule says it must.

IBM notes that agents use tool calling to get up-to-date information, optimize workflows and create subtasks on their own. Memory is optional: a simple agent forgets after each task, while a more advanced one remembers each person it works with.

Agentic AI is AI built to achieve a goal, not just answer a prompt. AWS defines it as “an autonomous AI system that can act independently to achieve pre-determined goals,” and says it typically builds on multiple specialized agents, each focused on a narrow area. IBM says agentic AI focuses on achieving goals by planning, making decisions and running multi-step workflows.

Researchers draw the same line. A 2025 paper by Sapkota, Roumeliotis and Karkee, AI Agents vs. Agentic AI, describes AI agents as modular systems for task-specific automation, and agentic AI as a step beyond: several agents collaborating, breaking tasks down as they go, keeping memory and coordinating their actions.

Be aware that “agentic” is also a marketing word. The Associated Press reported in November 2025 that at least one AI researcher it interviewed objects to calling any simple automated action agentic, and that the idea of software agents goes back to the 1990s. Judge a product by what it can actually do on its own, not by the label.

The three terms overlap because they stack: generative AI models do the thinking inside most agents, and agentic systems coordinate agents. This table separates them.

Generative AI makes things; agentic AI gets things done. IBM puts it this way: generative AI creates new content such as text, images, video, audio and code based on an input, and is primarily reactive. Agentic AI focuses on a goal, can proactively work toward it, and is designed to interact with tools, data sources and other systems.

They are not rivals. AWS notes that the reasoning inside agentic systems is powered by large language models, which are generative AI. So an agentic system usually has generative AI at its core, plus tools, memory, rules and a loop that keeps going until the goal is met.

A quick test: ask what happens after the first answer. If nothing happens until you type again, it is generative AI. If the system takes the next step itself, such as looking something up, calling a tool or trying again, it is acting as an agent. If it plans many of those steps toward a goal you set, often across several agents, it is agentic AI.

The easiest way to tell them apart is to look at the same job done three ways. Take a fitness coach who sells training plans online.

• Generative AI: the coach types “write a 4-week beginner plan” and gets a plan back. Nothing else happens until they ask again.
• AI agent: a support agent on the coach’s site reads a client’s message, looks up their plan and payment, and replies. It handles that one conversation.
• Agentic AI: the coach sets a goal such as “win back clients who stopped paying.” One agent finds them, another drafts a message, another prepares a discount, and the work waits for the coach to approve before anything is sent.

IBM’s own examples of simpler agents are everyday ones: a thermostat that follows rules, a robot vacuum that remembers what it cleaned, a navigation app that plans the fastest route.

Start with generative AI for content, add agents for repeated tasks, and use agentic setups only where you can set clear rules and approvals.

You can also put agents inside the product you sell, for example an AI meal planner or tutor for your audience. The AI app builder page shows how Starty adds them in one click. If you want to set up agents for other businesses, see how to start an AI automation agency.

The risk grows with autonomy: the more an AI can do without you, the more damage a wrong step can cause. An agent that can send emails can send the wrong one. One that can issue refunds can refund the wrong person.

• Keep a human approval step for anything that sends, charges, refunds, publishes or deletes.
• Give each agent only the tools and data it needs.
• Make sure you can see what every agent did and undo it.
• Watch the cost: each step is a model call, and multi-agent work uses more of them.