AI Agents Explained: How They Think, Act, and Get Things Done

An AI agent is software that takes a goal, plans the steps, uses real tools to carry them out, and checks its own results — adjusting when something fails. A chatbot answers. An agent acts.

Key takeaways An AI agent plans, acts, and self-corrects. A chatbot only responds. Automation runs fixed steps; an agent decides its next step based on what it finds. Agents need tools — an API, a browser, a database — or they’re just a language model. The main new risk is prompt injection: hidden instructions that hijack the agent mid-task. Autonomy multiplied by access equals risk. Both dials matter.

Every AI company on earth is currently calling something an “agent.” Your email tool. Your CRM. That chatbot that still just answers FAQs.

The word has been stretched until it barely means anything. So let’s fix that.

What Is an AI Agent?

An AI agent is autonomous software that receives a goal, breaks it into steps, executes those steps using external tools, and verifies its own output — looping until the task is finished.

Everything else is detail.

Here’s the gut-check. Ask ChatGPT to write a follow-up email, and you get text back. You still copy it, check the client’s last order date, paste it into your CRM, and hit send.

Ask an actual agent to “follow up with this client,” and it does all of that itself. Same underlying model. Wildly different amount of work left for you.

The GPS Analogy

A GPS is the cleanest non-technical example of agent behaviour.

You give it a destination. It doesn’t ask which streets to take — it reads traffic, picks a route, and quietly recalculates when you miss a turn.

Swap the routing math for a large language model, and you have a modern AI agent. Same perceive-decide-act cycle, except the destination can now be something fuzzy like “find me a cheap flight” instead of a fixed address.

That fuzziness is exactly what LLMs changed.

Agentic AI vs Agent: A Distinction Worth Stealing

The people building these systems draw the line in a specific place.

Anthropic’s engineering team splits agentic systems into two categories in its guide to building effective agents:

• Workflows — the steps are defined in code ahead of time
• Agents — the model decides its own path and tool use as it goes

Most products marketed as agents are workflows. That’s not an insult. It’s just a different thing, and knowing which one you’re buying matters.

How Do AI Agents Work?

An AI agent works in a loop: it interprets the goal, decomposes it into tasks, calls tools to execute them, evaluates whether the result met the goal, and retries a different way if it didn’t.

That evaluation step is the whole trick. Let’s watch it happen.

The Agent Loop, Step by Step

Say you tell an agent: “Find me a flight to Dubai next weekend, under $400, and hold the best option.”

1. Perceive — it reads the request and pulls in context: your calendar, saved preferences.
2. Plan — it decomposes “find a good flight” into search, filter by price, compare sites.
3. Act — it calls a flight API or opens a browser, instead of hallucinating a plausible price.
4. Verify — did it actually find something under $400? If not, it widens the search or tries another airline.
5. Report — it holds the seat and comes back to you, rather than booking and hoping you’re fine with that.

A script runs the same three moves whether they work or not. An agent notices failure and tries something else.

That’s the line between automation and a real agent.

What “Tool Use” Actually Means

Step 3 deserves a pause, because tool access isn’t magic. Somebody has to wire those tools in.

Tool use — also called function calling — is the mechanism that lets a model reach outside itself to an API, a database, or a browser. Without it, an LLM can only produce text.

There’s now an open standard for the plumbing: the Model Context Protocol, released by Anthropic and since adopted more broadly.

If you’ve wondered why one agent can read your Google Drive and another can’t, this is the layer where that gets decided.

AI Agent vs Chatbot vs Automation: What’s the Difference?

A chatbot answers a question and stops. Automation runs identical predefined steps every time. An AI agent plans multiple moves, uses tools, and chooses its own next action based on results.

Three questions settle it every time:

1. Does it plan more than one move ahead?
2. Does it use outside tools?
3. Does it decide its own next step?

A real agent is yes to all three.

	Plans ahead?	Uses tools?	Decides on its own?
Chatbot	Rarely	Sometimes	No
Automation (Zapier, RPA)	No — hardcoded	Yes, but fixed	No
AI agent	Yes	Yes	Yes

Why Automation Gets Mistaken for an Agent

This is where most people get tricked.

“New form submitted → send email → add to CRM” runs identically whether the submission is a hot lead or obvious spam. That’s workflow automation wearing a fancy label.

An actual agent reads the form, judges whether it’s worth chasing, and does something different depending on the answer.

Robotic process automation (RPA) has the same tell: it repeats; it doesn’t reason.

What Are the 5 Types of AI Agents?

The five classic types are simple reflex, model-based, goal-based, utility-based, and learning agents — a taxonomy from AI research that predates modern chatbots by decades.

It comes from Russell and Norvig’s Artificial Intelligence: A Modern Approach. You can read the intelligent agents chapter for free.

Type	What it does	Everyday example
Simple reflex	Reacts to now, remembers nothing	Thermostat
Model-based	Keeps some context	Robot vacuum
Goal-based	Picks whatever reaches the target	GPS
Utility-based	Finds the best option, not just a working one	Ride-share pricing
Learning	Improves with use	Streaming recommendations

Modern AI tools blur all five: goal-based planning, an LLM reasoning instead of hardcoded rules, sometimes learning from feedback.

What Is a Multi-Agent System?

A multi-agent system splits one job across several specialised agents that hand off work between them.

One researches, one writes, one checks the output.

Anthropic calls one version the orchestrator-workers pattern, where a central model breaks the task up and delegates pieces.

Think freelancer-doing-everything versus a small team where everyone has one job.

AI Agent Examples: What They Actually Do

Four patterns cover most of what’s shipping today.

• Coding agent — reads your codebase, writes a fix, runs it, and fixes what breaks, without pinging you every five minutes.
• Customer support agent — pulls up the order, checks the return policy, issues the refund. No “let me transfer you.”
• Research agent — takes a broad question, splits it up, digs through sources, returns a summary with receipts.
• Scheduling agent — checks everyone’s calendar and books the slot that works, instead of the twelve-email back-and-forth.

ChatGPT and Claude: Where Does the Agent Start?

A normal back-and-forth isn’t an agent loop, no matter how good the answer is.

A chat model becomes agent-like only once it’s given a goal, granted tools, and allowed to take multiple actions on its own.

Both companies ship separate agent products on top of their chat models. OpenAI has an agent mode inside ChatGPT that browses and acts on your behalf. Anthropic has Claude Code for software work.

These get renamed constantly. Treat the vendor’s help centre as the source of truth on what’s live today — not a blog post, ours included.

What about Alexa? It plans, but only inside a narrow lane: music, timers, smart lights. A goal-based agent on a leash rather than the open-ended kind.

Are AI Agents Safe? The Risks Nobody Advertises

Because an agent acts rather than talks, its mistakes are real actions — sent emails, spent money, deleted files — not just wrong answers.

That changes the risk profile completely.

What Is Prompt Injection?

Prompt injection is when hidden instructions planted in a webpage, email, or document hijack an agent mid-task and redirect it to do something else.

It’s the genuinely new risk here, and it isn’t hypothetical.

OpenAI’s support documentation for ChatGPT agent describes a scenario where a malicious comment picked up during a task tries to make the agent fetch a password reset code from Gmail and send it to an attacker’s site.

That’s the vendor documenting the failure mode in its own product help pages.

It’s also LLM01 — the top entry — in OWASP’s Top 10 for LLM applications, the standard security checklist for this category.

Why Prompt Injection Isn’t Solved

OpenAI has said publicly it may never be fully eliminated for browser agents.

Its own explainer on the attack lays out why it’s structurally hard: the agent can’t reliably distinguish instructions it was given from instructions it merely read somewhere.

A security researcher quoted in that TechCrunch piece frames the risk as autonomy multiplied by access. It’s the most useful two-variable test you can apply to any agent you’re evaluating.

Four Guardrails Every Agent Should Have

• Permission scoping — it can only touch what it’s supposed to
• Approval checkpoints — human-in-the-loop before payments, deletions, or emailing strangers
• Spend and rate caps — it can’t run away with your budget
• Audit logging — you can reconstruct exactly what it did

 

Our take: if a vendor can’t tell you which of these four they’ve implemented, that’s your answer about how carefully they’ve thought about it.

When Should You Use an AI Agent?

Use an agent when the path can’t be predicted in advance. Use a script when it can.

Predictable job, same steps every time, no judgment calls? You don’t need an agent. You need automation, and it’ll be cheaper and more reliable.

Anthropic gives the same advice: start with the simplest thing that works, add autonomy only when the task demands it.

Skip the Agent When

• One API call already does the job
• Every step must happen identically, no exceptions
• A mistake would genuinely hurt, and there’s no time to review

Reach for One When

• The task spans multiple tools or systems
• What happens next depends on what it finds along the way
• You’d rather review a finished result than supervise every step

Not sure which side your workflow falls on? Our Should I Use an Agent? test asks the three questions that matter, and the Agent Risk Score rates how safe it is to hand over.

Three Myths About AI Agents

“Any chatbot counts as an agent now.” Not without tools and a plan. Answering questions isn’t acting.

“Agents run with zero human involvement.” Most operate inside a budget, a scope, or an approval gate somebody deliberately set.

“More autonomy is always better.” More autonomy means mistakes happen faster and bigger when nobody’s watching.

FAQs

Q. What is an AI agent in simple terms?

An AI agent is software that takes a goal, plans the steps, uses tools to complete the task, and checks its results. Unlike a chatbot that mainly responds to prompts, an AI agent can take multiple actions and adjust its approach when something goes wrong.

Q. What is the difference between an AI agent and an LLM?

An LLM generates and understands text, while an AI agent uses an LLM as its reasoning engine to take actions toward a goal. An agent adds tools, instructions, context, and an action loop so it can perform tasks rather than simply generate a response.

Q. Do AI agents need memory?

No, memory is not required for an AI agent. An agent can complete a task using only the context available during that task. Memory becomes useful when an agent needs to remember users, preferences, previous actions, or information across multiple tasks.

Q. Can AI agents work together?

Yes. Multiple AI agents can work together in a multi-agent system. Different agents can handle specialized jobs — such as research, writing, coding, or reviewing — while passing information and results between them.

Q. Can AI agents browse the internet?

AI agents can browse the internet only when they have access to a web browser or browsing tool. An AI model does not automatically have internet access. The tools and permissions provided to the agent determine what websites, data, or online services it can access.

Q. What is tool use or function calling in AI agents?

Tool use, often implemented through function calling, allows an AI model to interact with external software and services. These tools can include APIs, databases, browsers, code interpreters, or business applications. Tool access is what allows an agent to move beyond generating text and actually perform tasks.

Q. What is the biggest risk of AI agents?

Prompt injection is one of the biggest security risks for AI agents. A malicious instruction hidden in a webpage, email, or document can potentially influence an agent and cause it to take an unintended action. Other risks include excessive permissions, accidental data exposure, unauthorized transactions, and errors being turned into real-world actions.

Q. Are AI agents the same as RPA?

No. RPA automates predefined steps, while an AI agent can decide what to do next based on the situation. RPA is well suited to repetitive, predictable processes. AI agents are more useful when a task involves changing conditions, multiple tools, or decisions that cannot be fully defined in advance.

The Bottom Line

An AI agent takes a goal, plans, acts with real tools, and verifies its own work — looping until the job is actually done, not just described.

Chatbots talk. Automation repeats. Agents adjust.

Next time something calls itself an AI agent, that’s the test that tells you whether it earns the name.

For more, visit: Agent Intellect

Disclaimer: AI agent capabilities, features, pricing, and availability can change frequently. This article explains the underlying concepts and uses official vendor documentation where current product details matter. Check the linked vendor sources for the latest information.