What is an AI Agent? How AI Agents Work
AI has traditionally been used to answer questions, generate content, classify information, or make predictions.
AI agents are different.
An AI agent is designed to take a goal and work toward completing it. It can decide which information it needs, choose a tool, perform an action, inspect the result, change its approach when something fails, and continue until the task is complete or until it needs a human to take over.
That distinction is becoming especially visible in software development.
For example, asking an LLM:
“Write a Python function that validates an email address.”
is a generation task.
Asking a coding agent:
“Find why the authentication tests are failing, fix the issue, run the test suite, and prepare the changes for review.”
is an agentic task.
The second request requires the system to inspect a codebase, reason about the problem, modify files, execute commands, observe results, and potentially repeat the process.
Products such as Anthropic Claude Code, OpenAI Codex and Google Antigravity demonstrate this shift toward task-oriented, agentic software development.
Anthropic’s Claude Code takes an agentic approach to software development, allowing developers to delegate coding tasks to an AI agent that can understand a codebase, edit files, run commands, execute tests, and work through problems from the terminal. OpenAI describes Codex as an AI coding agent that can work on engineering tasks, while Google positions Antigravity as an agent-first development platform where agents can plan, execute, and verify complex tasks across the editor, terminal, and browser.
So, what exactly is an AI agent?
And more importantly, how does an AI agent actually work?
Table of Contents
What is an AI Agent?
An AI agent is a software system that uses an AI model to understand a goal, reason about the next step, interact with tools or an environment, observe the result, and continue taking actions until the task reaches an acceptable outcome.
The important word here is system.
An AI agent isn't simply an LLM.
An LLM is the reasoning and generation engine. The agent is the larger system around that model.
A simplified view is:
AI Agent = Model + Context + Tools + Memory + Instructions + Orchestration + Environment + Guardrails
The exact architecture varies, but the principle is the same: the model makes decisions, while the surrounding system gives it the ability to do something about those decisions.
The simplest mental model
Think of a conventional chatbot as:
User → Model → Answer
An AI agent looks more like:
User → Agent → Model → Tool → Result → Model → Tool → Result → Final Outcome
The model may go through this loop several times before responding.
That loop is what makes an agentic system interesting.
AI Assistant vs AI Agent
The terms AI assistant, AI agent, AI chatbot, and generative AI are often used interchangeably. They shouldn't be.
Traditional chatbot
A chatbot primarily handles conversation.
User:“Explain REST APIs.”
AI:Provides an explanation.
AI assistant
An assistant can have additional context and tools.
User:“Summarize these five documents.”
AI:Reads the documents and creates a summary.
AI agent
An agent is given an outcome and can determine the steps required to achieve it.
User:
“Analyze these five documents, identify the major risks, compare them against our policy, create a report, and save it to the project folder.”
Now the system may need to:
Read the documents.
Identify relevant information.
Retrieve the policy.
Compare the findings.
Determine the important risks.
Generate the report.
Save the report.
Verify that the file was created correctly.
The difference isn't simply that the agent produces a longer answer.
The difference is that the agent is responsible for progressing the task.
How Do AI Agents Work?
At a high level, an AI agent follows an iterative loop:
Goal → Understand → Plan → Act → Observe → Evaluate → Repeat
This is sometimes called an agent loop.
Let's look at what happens inside it.
The Agent Receives a Goal
Everything starts with an objective.
For example:
“Fix the checkout bug in our application and make sure the existing tests still pass.”
The agent now has an outcome to achieve.
Importantly, the user doesn't necessarily specify every individual action.
They don't say:
“Open checkout.py, inspect line 143, change function X, run pytest, read the error, modify function Y…”
The agent has to determine the path itself.
The Agent Builds Context
Before taking action, the agent needs to understand its environment.
For a coding agent, that might mean examining:
Repository structure
Source files
Configuration
Dependencies
Tests
Documentation
Git history
Existing errors
Developer instructions
For a customer-service agent, context might include:
Customer profile
Previous conversations
Order history
Company policies
Product information
This is where context engineering becomes important.
An agent cannot make good decisions about information it cannot access.
The Model Reasons About the Task
The AI model then determines what should happen next.
Suppose a coding agent receives:
“Fix the failing payment test.”
The agent may reason that it needs to:
Locate the failing test.
Run the test to reproduce the problem.
Inspect the relevant implementation.
Determine the likely cause.
Modify the code.
Run the test again.
Check for regressions.
The important point is that the plan can change.
If the first hypothesis is wrong, the agent can investigate further rather than simply returning an answer.
The Agent Selects a Tool
This is one of the biggest differences between an LLM and an agent.
An LLM can suggest:
“Run the test suite.”
An agent with a terminal tool can actually run it. Tools might include:
Web search
Browser
Terminal
Code interpreter
Database
REST API
CRM
Email
Calendar
File system
Cloud infrastructure
Internal business applications
For example: Model → "I need to inspect the repository" → Filesystem Tool → Returns project files
The model then receives the result and decides what to do next.
The Agent Takes an Action
The selected tool performs the action.
For a coding agent: Agent → Read checkout.py → File system → Source code returned
For a research agent: Agent → Search for competitor pricing → Search Tool → Search results returned
For a customer-service agent: Agent → Check order #0000001 → Order API →
The agent is effectively interacting with an external environment.
The Agent Observes the Result
The tool returns information. This result becomes new context for the model.
For example:
Run tests
↓
127 tests executed
↓
3 tests failed
↓
Agent analyzes failuresThe agent now has new evidence.
It can decide:
“The failures appear to come from the same authentication middleware. I should inspect that component.”
This is where an agent becomes iterative rather than one-shot.
The Agent Evaluates Its Progress
The agent needs to determine whether the task is finished.
For example:
Run tests
↓
3 failures
↓
Modify code
↓
Run tests
↓
1 failure
↓
Investigate
↓
Modify code
↓
Run tests
↓
All tests passThe system can now conclude that the original objective has been achieved or at least that the defined success criteria have been met.
The Loop Repeats
The complete process can therefore look like:
This loop is the heart of many modern agentic systems.
AI Agent Architecture
A practical AI agent architecture typically contains several layers.
The Model
The model is responsible for understanding language, reasoning about the task, deciding what information is needed, selecting tools, and producing outputs.
But the model alone doesn't have to be the entire agent.
The agent runtime determines how the model interacts with the environment.
Tools
Tools are what allow an agent to move from thinking to doing.
For example:
Tool: search_web()
Tool: read_file()
Tool: write_file()
Tool: run_command()
Tool: query_database()
Tool: send_email()
Tool: create_calendar_event()The model decides when a tool is appropriate. The runtime executes it. The result goes back to the model.
Memory and State
An agent needs to know what has already happened.
Imagine a coding task that requires 20 tool calls. The agent needs to maintain information such as:
What files it inspected
What changes it made
Which tests failed
Which hypotheses were rejected
What remains to be completed
This is state.
Some systems also maintain longer-term memory that can persist beyond a single task.
Instructions
Agents typically operate with instructions that define:
Their role
Available tools
Rules
Constraints
Expected output
Security boundaries
When to ask for human approval
For example:
“You are a software-engineering agent. Do not modify production infrastructure without explicit approval. Run tests before reporting a task as complete.”
These instructions influence how the agent operates.
How a Coding Agent Works
Let's make the architecture concrete with a real example.
Suppose a developer gives Codex the task:
“The checkout API is returning HTTP 500 for users with expired cards. Find the bug, fix it, add a regression test, and verify the solution.”
A typical agentic workflow could look like this.
Step 1: Understand the repository
The agent inspects the project structure.
src/
tests/
payments/
checkout/
config/Step 2: Locate the relevant code
It searches for:
checkout
payment
expired card
HTTP 500Step 3: Reproduce the problem
It runs the relevant test or application command.
pytest tests/test_checkout.pyThe test fails.
Step 4: Investigate
The agent follows the error through the code. It might discover that an expired-card exception isn't being handled correctly.
Step 5: Modify the implementation
It changes the relevant code.
Step 6: Add a regression test
Instead of fixing only today's failure, the agent adds a test that should prevent the bug from returning.
Step 7: Run the tests
The agent run the tests
pytestSuppose:
246 passedStep 8: Review the changes
The agent checks the resulting diff and ensures that the change didn't introduce unnecessary modifications.
Step 9: Report the result
The developer receives:
What was changed
Which files changed
Why the bug occurred
Which tests were added
Test results
Any remaining concerns
That is substantially different from:
“Here's some Python code that might fix your problem.”
The agent is participating in the software-development workflow, not simply generating code.
Codex can also run coding tasks in cloud environments, work across editor and terminal surfaces, and support multi-agent workflows.
When Should You Use an AI Agent?
Not every AI problem needs an agent.
In fact, adding an agent to a simple task can make a system more expensive, slower, and less reliable.
An agent makes more sense when a task has several of these characteristics:
Multiple steps
Unstructured inputs
Changing conditions
Tool usage
Decision points
Exceptions
Need for adaptation
Human approval at specific stages
For example:
Probably not an agent
“Convert this JSON into CSV.”
A normal program is better.
Maybe an LLM
“Summarize this document.”
A standard LLM workflow may be enough.
Good candidate for an agent
“Review these customer complaints, identify which ones require refunds, check the order system, verify the refund policy, and prepare the appropriate actions for approval.”
This requires interpretation, tools, decisions, and multiple steps.
That's where agentic architecture starts to make sense.
The Biggest Challenge: Giving Agents Too Much Freedom
Agent autonomy sounds attractive. But autonomy without boundaries can become a liability.
Imagine giving an agent access to:
Production databases
Company email
Cloud infrastructure
Financial systems
Customer records
and telling it:
“Do whatever is necessary.”
That's not a production architecture.
A reliable agent needs boundaries. These can include:
Tool permissions
Sandboxed environments
Approval gates
Spending limits
Read/write restrictions
Authentication
Data-access policies
Human escalation
Audit logs
Monitoring
OpenAI's discussion of running Codex safely, for example, emphasizes technical boundaries, approval requirements, system access controls, and telemetry for understanding and auditing agent behavior.
Why AI Agents Can Fail
Agentic systems introduce failure modes that a normal chatbot may not have.
Model Failure: The model may misunderstand the task.
Tool Failure: An API may return an error or unexpected result.
Planning Failure: The agent may choose an inefficient or incorrect sequence of actions.
Context Failure: The agent may not have the information required to make a good decision.
Verification Failure: The agent may incorrectly conclude that the task is complete.
Permission Failure: The agent may attempt an action it shouldn't be allowed to perform.
Security Failure: An external input could attempt to manipulate the agent into violating its instructions.
This is why “the model is smart” is not enough to build a reliable agent. Production agents need engineering around the model.
What Makes an AI Agent Reliable?
A production-quality agent needs more than a powerful model. It needs a strong surrounding system.
Good tool design
Tools should have clear inputs, outputs, permissions, and failure behavior.
Strong context
The agent should receive the information it actually needs without overwhelming its context window.
Verification
Important actions should have a way to verify whether they succeeded.
Guardrails
High-risk actions should require additional checks or human approval.
Observability
Teams should be able to understand:
What the agent did
Which tools it called
What information it received
Why it failed
How much it cost
How long it took
Evaluation
Agents should be tested against realistic tasks rather than judged only by individual model responses.
This is a fundamental change in how AI systems are evaluated.
Instead of asking:
“Did the model produce a good answer?”
we increasingly need to ask:
“Did the system successfully complete the task?”
The Future of AI Agents
The most important development in AI agents isn't simply that models are becoming better.
It is that models are increasingly being connected to tools, environments, memory, and software systems.
That changes the role of AI.
The first generation of generative AI largely answered questions.
The next generation increasingly helps execute work.
A developer might say:
“Modernize this legacy service.”
A researcher might say:
“Investigate this market and produce a report.”
A sales manager might say:
“Identify the highest-value leads and prepare follow-ups.”
A business user might say:
“Find why our revenue dropped last month and prepare an executive summary.”
The common pattern is: Goal/ Intent → Reasoning → Tools → Actions → Feedback → Verification
That is the foundation of agentic AI.
An AI agent isn't simply a smarter chatbot. It is a goal-oriented system built around an AI model that can reason, use tools, take actions, observe results, and adapt its next step.
The most useful way to think about the technology is:
LLM: Generates and reasons.
Assistant: Helps the user.
Workflow: Follows predefined steps.
Agent: Decides and acts within a defined environment.
Multi-agent system: Coordinates multiple specialized agents.
And the real engineering challenge isn't just making an agent autonomous. It's making it reliable, observable, secure, and useful enough to trust with real work.
That is why the future of AI agents isn't simply about giving models more autonomy. It's about building better systems around them.
Frequently Asked Questions (FAQs)
What is an AI agent in simple terms?
An AI agent is a software system that uses artificial intelligence to understand a goal, make decisions, use tools, and take actions to accomplish that goal.
How does an AI agent work?
An AI agent typically receives a goal, understands the context, creates a plan, uses available tools, observes the results, and repeats the process until the task is completed or human intervention is required.
What is the difference between AI and an AI agent?
AI is a broad field that includes technologies capable of performing tasks associated with human intelligence. An AI agent is a specific type of AI-powered system designed to perceive information, make decisions, and take actions toward a goal.
Are AI agents fully autonomous?
Not necessarily. Some AI agents can perform tasks with significant autonomy, while others require human approval at important steps. The level of autonomy depends on how the system is designed and what permissions it has.
What are some examples of AI agents?
Examples include customer-service agents, coding agents, sales agents, research agents, personal assistants, and workflow automation agents.
Can AI agents replace human workers?
AI agents can automate certain tasks and workflows, but they do not automatically replace entire jobs. Their impact depends on the type of work, the quality of the AI system, and how organizations deploy it. Human judgment remains important for many complex or high-stakes decisions.
