What Is an AI Agent and how does It work?

AI agents are the most exciting advancement in work productivity, promising to help us to achieve better results, faster.

But what even is an AI agent?

For most non-developers, it's actually pretty confusing. Plenty of vendors call their AI chatbot an agent. Your marketing automation tool probably has an "agent" now too.

Emily Kramer, who writes one of the sharpest marketing newsletters on the internet, recently observed that 90% of marketers are still trying to understand what an agent even is, let alone build one.

That confusion is expensive. It leads to buying the wrong tools, scoping the wrong projects, and setting expectations that guarantee disappointment.

So let's fix that!

What is an AI agent?

An agent operates using the intelligence of an LLM, the execution capabilities you give it access to, and the knowledge you equip it with.

Using the elements we detail below, AI agents can competently pursue a goal across multiple steps, deciding what to do next based on what it learns along the way:

1. An agent has a goal. Not just "answer this question" but "figure out why our conversion rate dropped last week and give me a report with recommendations." The goal is the outcome, not a single response.

An agent takes multiple steps. An agent doesn't just generate text and stop. It can query your database, read the results, decide it needs more context, pull data from another system, run calculations, and synthesize everything into an answer. Each step informs the next.

An agent decides what to do. This is the critical part. The agent isn't following a script you wrote. It's reasoning about what action to take based on what it's learned so far. If the first data pull doesn't answer the question, it figures out a different approach.

Compare that to how most people think of an agent as a chatbot that's slightly better at following instructions. The gap between those two mental models explains most of the confusion in the market right now.

AI agent as a marketing term

"AI agent" has become a marketing term applied to everything from a smart chatbot to a fully autonomous system. It helps to think about AI tools on a spectrum.

Chatbots answer questions. You ask, they respond. One turn, one output. They don't take action or make decisions. If your "agent" can only respond to direct questions in a chat window, it's just a chatbot.

AI-powered workflows execute a fixed sequence with AI at one or more steps. Think of a Zapier or n8n automation where one step uses GPT to classify an email before routing it. The logic is pre-built. The AI makes a decision within a single step, but the overall flow is determined by you.

But AI agents reason through multi-step problems and decide their own path. They choose which tools to use, in what order, and adjust their approach based on intermediate results. Instead of following your pre-determined flowchart, they're building their own.

Here's a practical test. If you can draw the workflow as a flowchart before the agent runs, it's not really an agent. It's an automation with AI inside it. And that's valuable for many use cases. However, calling it an agent sets wrong expectations.

Why the distinction matters for your business

The confusion between chatbots, workflows, and agents is not a semantic debate. It directly affects how companies invest in and adopt AI.

When someone says, "we deployed an AI agent" and it's really a chatbot, leadership sees limited ROI and concludes agents don't work. Or when someone builds a rigid workflow and calls it an agent, the team gets frustrated when it can't handle edge cases or by how much hand-holding it requires.

MIT's research on enterprise AI adoption found just last year that only 5% of AI pilots are generating meaningful value. The researchers point to "approach" as the main differentiator, not model quality or regulation. Starting with the right mental model is key to seeing results from an investment in AI.

Later in 2025, an AI study from Wharton reported that those numbers were quickly changing. Three out of four large businesses were seeing results. However, the bottleneck is still largely culture and workflow.

And last but not least, OpenAI's enterprise AI adoption report verified what we see daily across teams. The most AI-enabled users (what OpenAI calls "frontier users") are pulling away with strong gains in productivity. These are the users with the wisdom to know the difference between AI agent and chatbot, but they're still a minority in most organizations.

The job description test

Emily Kramer offers what might be the most practical framing for business users. Think of building an agent like writing a job description. Define the What, the When, and the How before you open any tool.

This framing works because it maps to how people already think about delegating work. When you hire someone, you don't hand them a script for every scenario. You define the role, the goals, and the guardrails, then let them work out how to get it done. An agent operates the same way.

The job description test also helps you decide whether you actually need an agent at all:

• If the task has a fixed, predictable flow: use a workflow automation.
• If the task requires answering one-off questions: use a chatbot or an AI assistant.
• If the task requires judgment, multiple tools, and adapting to what comes back: that's an agent.

Most business processes fall somewhere in the middle, which is why the best AI platforms let you start with a simple prompt and scale toward more autonomous behavior as you build trust.

What AI agents look like in practice

Abstract definitions only go so far. Here's what agents actually do inside companies today:

A sales team gives an agent access to their CRM, email, and call recordings. Before each meeting, the agent pulls the prospect's recent activity, checks for open support tickets, reviews the last three calls, and prepares a briefing document. No one told it which systems to check or in what order. It determined the best approach based on what was available.

A marketing team asks an agent to monitor competitor pricing pages weekly. The agent crawls the pages, compares changes against the previous week, cross-references against the team's positioning document, and flags anything worth discussing. When a competitor drops their enterprise price by 20%, the agent surfaces it in Slack with context on how it affects current deals.

An ops team sets up an agent to reconcile data across their billing system, CRM, and accounting software. Instead of a fixed ETL pipeline, the agent handles edge cases, like mismatched customer names or currency conversion discrepancies, by reasoning through them rather than failing silently.

A product manager uses a triage agent to review every open issue across Linear, GitHub, and those reported in Slack to assign open issues to sprints on a weekly basis, based on whether they are aligned with the near-term product vision, or backlogs them for later revisiting. It then closes out all open duplicates in favor of one canonical work item that can be assigned to a developer, or another agent.

In each case, the value isn't in any single AI output. It's in the agent's ability to work across systems, make decisions about what matters, and deliver a result that would have taken a human hours of context-switching.

Building agents without building software

The biggest shift happening right now is that building an agent no longer requires writing code or stitching together five different tools. The gap between "I have a business problem" and "I have an agent solving it" is shrinking.

Adapt is built on this principle. Instead of requiring you to wire together an LLM with a tool integration layer, a scheduler, and a monitoring system, Adapt gives you a single platform where you describe what you need and the system handles the planning and delivery.

You connect your tools, such as a CRM, data warehouse, docs, code repos, and communication platforms. Then you can describe the work in natural language, the same way you'd brief a new hire. And the system reasons through how to get it done, using the tools and data available to it.

The result is that one person can operate like a small team.

If you're trying to understand where agents fit in your organization, or if you've been burned by tools that promised "agents" but delivered chatbots, get a demo of Adapt. You will see what agents look like when they're connected to your real business context.

FAQ

What is an AI agent in simple terms?

An AI agent is an LLM-powered software that works toward a goal by taking multiple steps, using tools, and deciding what to do next.

What is the difference between an AI agent and a chatbot?

A chatbot responds to direct questions with a single answer. An AI agent pursues a multi-step goal, choosing which tools to use and adapting its approach based on intermediate results.

What is the difference between an AI agent and an AI workflow?

An AI workflow follows a predetermined sequence of steps, where each step might use AI. An AI agent determines its own sequence of steps based on the goal and what it discovers along the way.

Do I need an AI agent for my business?

Not necessarily. If your process is predictable with clear inputs and outputs, a simple automation or workflow is faster and more reliable. Agents are best for tasks that span multiple systems, require judgment about what to do next, and involve synthesizing information from different sources. The best approach is to start with your business problem and work backward to the right tool, rather than starting with "we need agents."

Are AI agents going to replace jobs?

Today Agents are augmenting how people work by handling repetitive, cross-functional tasks that consume disproportionate time. The goal is to let one person execute like a small team, not to eliminate the team. The most successful teams we work with are doing more with the experts they have by equipping them with agents vs eliminating headcount because they can.

How do I build an AI agent?

Start by writing a "job description" for the agent. Define what it should accomplish, when it should run, and what tools and data it needs access to. Then choose a platform that connects to your business tools and lets you describe work in natural language. Platforms like Adapt handle the orchestration, so you don't need to write code or stitch together multiple services.

What makes a good AI agent?

A good agent has a clearly scoped goal, access to the right tools and data, and appropriate guardrails. It should do one thing well before you expand its responsibilities. The best agents handle work that would take a human hours of context-switching across multiple systems, and they deliver results that are grounded in your actual business data rather than generic AI outputs.