Ask a chatbot for a refund and it explains the policy; an AI agent reads the ticket, checks the order, and puts the money back. An AI agent is a program that takes a goal, figures out the steps itself, uses tools to act, and keeps going until the work is finished. That one shift, from answering to acting, is the whole story, and it is why Gartner expects 40% of enterprise apps to ship a task-specific agent by the end of 2026, up from under 5% in 2025.
What Is an AI Agent?
Short answer. An AI agent is software powered by a large language model that pursues a goal on its own. It plans the steps, uses tools to take real actions, observes what happened, and adjusts, looping until the task is done or it needs a human. The short test: a chatbot answers a question, an agent completes a task.
That definition is not just ours. IBM describes an AI agent as a system that autonomously performs tasks on behalf of a user by designing its own workflow and using the tools available to it. Anthropic puts it even more simply: an agent is an LLM autonomously using tools in a loop, deciding for itself how to reach the goal instead of following a fixed script.
Hold onto that last word: loop. It is the part most definitions skip, and it is the part that makes an agent an agent. Without the loop you have a smart autocomplete. With it, you have something that can finish a job. For the wider picture, our complete guide to AI agents maps every type and use case.
How Does the AI Agent Loop Work?
Short answer. An agent runs a cycle: plan, act, observe, adjust. It reads the goal, decides the next step, calls a tool, looks at the result, then loops until the task is done or it hits a stopping point. Each pass uses what it just learned to choose the next move.
Picture the refund in the diagram above. A human did not script those steps. The agent read the ticket, decided to look up the order, saw it was in policy, checked the refund rules, then issued the money back. Four steps, each one chosen based on what the last one returned.
That is the core architecture of every agent, no matter the vendor. As Oracle lays out the agent loop: the agent gathers context, reasons about the best next action, executes it, observes the outcome, and feeds that observation into the next pass. It repeats until the task is complete or a stopping rule fires.
The widely-cited version of this is the ReAct pattern (reason plus act). The model thinks out loud about what it needs, takes an action, reads the result, and goes again. Every major lab, OpenAI, Anthropic, Google, and Microsoft, converged on the same loop while building very different products around it.
What Are the 5 Parts of an AI Agent?
Short answer. Every AI agent is built from five parts: a model that reasons, instructions that define its job, knowledge it can draw on, tools it can use to act, and a runtime where it lives and remembers. Change any one and the agent behaves differently.
You can build an agent yourself once you know the pieces. Anthropic calls the base unit an augmented LLM: a model enhanced with retrieval, tools, and memory. Spelled out for a builder, that becomes five parts.
The model is the brain that reads and reasons. The instructions are the job description, including what it must never do. The knowledge is your docs and data, so it answers from your facts instead of guessing. The tools are the hands: look something up, send an email, create a record, issue a refund. The runtime is where it all runs, holds memory, and connects to your systems.
AI Agent vs Chatbot: What Is the Difference?
Short answer. A chatbot answers in a single pass and then stops. An AI agent runs a loop and takes real actions, then checks the result and decides the next step. The difference is architecture, not how smart the reply sounds: a chatbot is a responder, an agent is an operator.
This is the question most people are really asking when they search “what is an AI agent.” The honest answer from Make’s breakdown and others is that the line is about doing, not talking. Here is the split:
| Chatbot | AI agent | |
|---|---|---|
| Core job | Answer a question | Complete a task |
| How it runs | One pass, then stops | A loop: plan, act, observe |
| Takes actions | No, it replies | Yes: refunds, emails, records |
| Uses tools | Rarely | Yes, that is the point |
| Handles multi-step work | No | Yes, breaks goals into steps |
| When stuck | Says it cannot help | Tries another step or asks a human |
Both have their place. A chatbot is the right tool when you only need an answer, like an FAQ widget. An agent is the right tool when the user wants an outcome, like the refund actually landing in their account. If you want the deep version, we wrote a full agentic AI vs generative AI comparison.
Is an AI Agent the Same as “Agentic AI”?
Short answer. No, but they are close. An AI agent is the specific program doing a job. Agentic AI is the broader idea of software that plans and acts toward goals with limited supervision. An agent is one instance of agentic AI in action.
The terms get used interchangeably, and that is mostly fine. The useful distinction: “agent” names the thing you build and deploy, while “agentic” names the behavior, the planning-and-acting property. A workflow with steps hard-coded by a developer is not agentic. An agent that decides its own steps is.
Anthropic draws this same line cleanly: workflows have control flow written in code, while agents put the LLM in charge of the control flow. That is why the same lab calls an agent, plainly, “an LLM autonomously using tools in a loop.” The loop, again, is the giveaway.
What Can an AI Agent Actually Do?
Real, shipping examples, not science fiction. Today an agent can:
- Resolve support tickets. Read a ticket, look up the order, apply policy, issue a refund, and draft the reply, like the run at the top of this page.
- Run sales development. Research a lead, score it against your ideal customer profile, write a first message, and follow up on a schedule.
- Enrich and clean data. Take a list of companies and fill in size, industry, and contacts, then flag the records that need a human.
- Handle inbox and ops chores. Triage email, schedule meetings, update a CRM, and route the edge cases to a person.
This is no longer a lab demo. Roughly 31% of enterprises now have at least one AI agent in production, led by banking and insurance at 47%. Want to see the build itself? Our guide on how to build an AI agent walks the five parts end to end, and the AI agent examples page shows ten concrete jobs across support, sales, and ops.
Now that you can name the five parts, you can assemble them. TinyAgents hands you the model, instructions, knowledge, tools, and runtime in one place, so you wire up a real working loop instead of reading about one. No code required. Free to start, $49/mo for the whole platform.
Build an AI agent free →What an AI Agent Cannot Do (Yet)
Short answer. Agents are good at scoped, rules-based jobs with clear data and tools. They are weak when the goal is vague, the data is missing, or nobody is watching the edge cases. Treat an agent as a capable junior teammate, not an unsupervised expert.
The failure modes are predictable. Give an agent a fuzzy goal and it wanders. Hide the data it needs and it guesses. Hand it a high-stakes action with no human checkpoint and a small mistake becomes a real one. The fix is not a smarter model: it is a narrower job and a clear fallback.
The governance gap is real, too. Deloitte found only about 1 in 5 companies running agents have a mature model for overseeing them, which means most teams are deploying faster than they are governing. Gartner now tracks agentic AI on its 2026 Hype Cycle, a useful reminder to scope tightly and keep a human on the loop while the tooling matures.
Our take, as people building this: start narrow. One job, grounded in your own data, with real tools and a clear human handoff. That recipe is identical across every platform, which is the whole reason we built TinyAgents so the agent sits next to the forms, tables, and email it needs instead of behind a wall of middleware.
Frequently Asked Questions
What is an AI agent, in simple terms?
An AI agent is a software program that uses a large language model to pursue a goal on its own, deciding the steps, using tools, and adjusting as it goes. Instead of answering one question and stopping, it runs a loop: plan, act, check the result, and try again until the job is done. IBM defines it as a system that autonomously performs tasks by designing its own workflow and using available tools. The short test: a chatbot tells you something, an agent does something.
What is the difference between an AI agent and a chatbot?
A chatbot answers in a single pass: you ask, it replies, the turn ends. An AI agent runs a loop and takes real actions, like issuing a refund, updating a record, or sending an email, then observes the outcome and decides the next move. The difference is architecture, not how smart the model sounds. A chatbot is a responder; an agent is an operator.
What are the main parts of an AI agent?
Every AI agent has five parts: a model (the LLM that reasons), instructions (its job and limits), knowledge (your docs and data), tools (the actions it can take), and a runtime (where it lives, remembers, and connects to your systems). Swap any one and the agent changes. The model thinks, the tools act, and the instructions keep it inside the rails you set.
Is an AI agent the same as agentic AI?
They are related but not identical. An AI agent is the specific program doing the work; agentic AI is the broader property of software that plans and acts toward goals with limited human oversight. Anthropic frames an agent simply as an LLM autonomously using tools in a loop. You can read the full breakdown in our agentic AI vs generative AI guide.
What can an AI agent actually do today?
Scoped, real jobs: answer support tickets and issue refunds, research and qualify sales leads, enrich records, send timed follow-ups, and update your CRM. The pattern that works is one narrow job, grounded in your own data, with real tools and a human fallback. Agents struggle when the goal is vague, the data is missing, or no human is watching the edge cases.