How AI Agents Actually Work

Written by Akhil Thirunalveli

118 views 20 min read Jun 2, 2026

Everyone Is Talking About Agents. Almost Nobody Explains Them Well.

If you have been anywhere near tech news in the past year, you have heard the word "agent" thrown around constantly. AI agents. Autonomous agents. Agentic workflows. Agent frameworks.

But if you asked ten people to define what an AI agent actually is, you would get twelve different answers.

Here is the thing — the core idea behind AI agents is not that complicated. It has been dressed up in marketing language and buried under layers of framework jargon, but the fundamental concept is something anyone can understand.

This blog is going to strip away all of that noise. By the end, you will understand what an AI agent is, how it works step by step, what makes it different from a regular chatbot, and why everyone is so excited (and sometimes worried) about them.

First, Let Us Get the Basics Straight

Before we talk about agents, let us make sure we are on the same page about what a large language model (LLM) does on its own.

An LLM like GPT-4, Claude, or Gemini is fundamentally a text prediction machine. You give it text, it gives you back text. That is it. It does not browse the web. It does not run code. It does not send emails. It does not remember what you said yesterday. It reads your input, generates a response, and stops.

Think of an LLM as a really smart person sitting in a sealed room. You can slide notes under the door, and they slide notes back. They are brilliant at writing, reasoning, and answering questions. But they cannot leave the room. They cannot make phone calls. They cannot open a browser. They are stuck with whatever you write on the note.

An AI agent is what happens when you give that person a phone, a laptop, a to-do list, and permission to act on their own.

What Makes an Agent an Agent?

A chatbot takes your input, generates a response, and waits for your next message. That is a single turn. Input, output, done.

An agent does something fundamentally different. It takes your input, and then it decides what to do next on its own. It might call a tool, read the result, think about it, call another tool, read that result, revise its plan, call yet another tool, and keep going until the task is done — all without you saying anything after the first message.

The key difference is autonomy in a loop.

A chatbot is like texting a friend who answers each message individually. An agent is like hiring a freelancer — you describe what you want, and they go figure out how to get it done, checking back with you only when they need to.

There are three things that turn a plain LLM into an agent:

Tools — the ability to take actions beyond just generating text
A reasoning loop — the ability to observe results, think, and decide what to do next
Memory and state — the ability to keep track of what has been done and what still needs doing

Let us dig into each one.

Part 1: Tools — Giving the LLM Hands

An LLM by itself can only produce text. Tools are what let it interact with the outside world.

A "tool" is just a function that the agent can call. Some common examples:

Web search — look something up online
Code execution — write and run a piece of code
File operations — read, write, or edit files on a computer
API calls — send a request to some external service (send an email, create a calendar event, post a message)
Database queries — look up or modify data in a database
Browser automation — navigate to a web page, click buttons, fill forms

Here is how tools work in practice. The LLM does not actually execute the tool itself. Instead, it outputs a structured request — essentially saying "I want to call this tool with these inputs." The system running the agent intercepts that request, executes the tool, and feeds the result back to the LLM.

Let me walk through a concrete example.

Say you ask an agent: "What is the weather in Tokyo right now?"

Here is what happens behind the scenes:

The LLM receives your question.
The LLM knows it has access to a get_weather tool. It generates a tool call: get_weather(location="Tokyo").
The agent system intercepts this, actually calls the weather API, and gets back: {"temp": 24, "condition": "partly cloudy"}.
This result is fed back to the LLM as a new message.
The LLM reads the result and generates a human-friendly response: "It is currently 24°C and partly cloudy in Tokyo."

The LLM never touched the weather API directly. It just said what it wanted to do, and the system did it. But from the user's perspective, the agent "looked up the weather."

This might seem like a small thing, but it is the single most important capability that separates an agent from a chatbot. Tools are the hands of the agent.

Part 2: The Reasoning Loop — Think, Act, Observe, Repeat

Tools alone do not make an agent. What makes an agent is the loop.

Imagine you ask an agent to plan a weekend trip to Goa. A chatbot would give you a generic response with some suggestions. An agent would actually start working on it:

Think: "I need to find flights, hotels, and activities. Let me start with flights."
Act: Calls a flight search tool.
Observe: Gets back flight options and prices.
Think: "Okay, the cheapest flight is on Saturday morning. Now let me find hotels near Baga Beach."
Act: Calls a hotel search tool.
Observe: Gets back hotel listings.
Think: "This hotel has great reviews and is within budget. Let me now find some restaurants and activities."
Act: Calls a search tool for restaurants and things to do.
Observe: Gets back results.
Think: "I have everything I need. Let me put together a complete itinerary."
Act: Generates a detailed trip plan and presents it to the user.

This loop — Think → Act → Observe → Think → Act → Observe — is the heartbeat of every AI agent. It is sometimes called the "ReAct" pattern (Reasoning + Acting), and it was described in a 2022 research paper that became hugely influential.

The important thing to understand is that the LLM is making decisions at each step. It is not following a predetermined script. After observing the result of each action, it decides what to do next based on what it has learned so far. If the flight search returns no results, it might try different dates. If the hotel is too expensive, it might search for alternatives. It adapts.

This is what makes agents feel "intelligent" — they are not just pattern-matching on your input. They are executing a dynamic plan that evolves based on real-world feedback.

Part 3: Memory — Keeping Track of Everything

There is a practical problem with the loop described above. Every time the agent "thinks," it needs to remember what it has already done, what results it got, and what it still needs to do.

This is where memory comes in, and it operates at multiple levels.

Short-Term Memory (The Context Window)

The most basic form of memory is the context window — the running transcript of everything that has happened so far in this agent session. Every tool call, every result, every thought the agent has had — it all sits in the context window.

This works fine for short tasks. But for longer tasks, the context window fills up. If the agent has made 30 tool calls, each with detailed results, that is a lot of tokens. The agent might start losing track of earlier information.

Working Memory (Scratchpads)

Some agent systems give the agent a "scratchpad" — a place to write down notes, plans, and intermediate results. Instead of relying on the full conversation history, the agent periodically summarizes what it knows and what it still needs to do, and writes that summary to the scratchpad.

Think of it like a detective working a case. The full case file might be hundreds of pages, but the detective keeps a small notebook with the key facts and next steps. The scratchpad is that notebook.

Long-Term Memory (External Storage)

For agents that operate over days, weeks, or across multiple sessions, some systems implement long-term memory. This is usually a database (often a vector database) where the agent stores important facts, decisions, and outcomes.

Before starting a new task, the agent can search its long-term memory for relevant past experiences. "Have I done something like this before? What worked? What did I learn?"

This is still an active area of research and development. Most agents today rely primarily on short-term memory (the context window) and some form of working memory. True long-term memory that works reliably is hard.

The Full Picture: How an Agent Runs End to End

Let me put all the pieces together with a real example.

Say you tell a coding agent: "Add a dark mode toggle to my website."

Here is roughly what happens:

Step 1 — Understanding the task The agent reads your request and thinks about what it needs to do. It identifies sub-tasks: understand the current codebase, figure out how styles are organized, implement the toggle component, add the theme switching logic, and update the CSS.

Step 2 — Research The agent uses file-reading tools to explore your project. It reads your main CSS file, your layout component, and your configuration files. It builds a mental model of how your code is structured.

Step 3 — Planning Based on what it learned, the agent formulates a plan: "I will create a ThemeProvider component, add CSS custom properties for light and dark themes, create a toggle button component, and wire it all together in the layout."

Step 4 — Execution The agent starts writing code. It creates new files, modifies existing ones, and uses tools to write to the filesystem. After each change, it might run the dev server or tests to verify things are working.

Step 5 — Verification The agent runs the application, checks for errors, and maybe even takes screenshots to verify the visual result. If something is broken, it reads the error messages, diagnoses the issue, and fixes it.

Step 6 — Reporting The agent presents the completed work to you, explaining what it did and why.

All of this happens in a loop. The agent is constantly cycling through think → act → observe, adjusting its approach based on what it finds.

The System Prompt: Shaping the Agent's Personality and Rules

Every agent starts with a system prompt — a set of instructions that define who the agent is, what it can do, and how it should behave.

The system prompt is like a job description. It tells the agent:

Role: "You are a senior software engineer who helps users with coding tasks."
Available tools: "You have access to these tools: read_file, write_file, run_command, search_web."
Behavioral rules: "Always explain your reasoning. Ask for clarification before making destructive changes. Write clean, well-documented code."
Constraints: "Do not access external APIs without user permission. Do not delete files without confirmation."

The quality of the system prompt has a massive impact on how well the agent performs. A vague system prompt produces a vague agent. A detailed, well-structured system prompt produces an agent that consistently delivers good results.

This is why "prompt engineering" for agents is a real skill. The people building the best agent products spend a lot of time crafting and iterating on their system prompts.

Function Calling: The Technical Mechanism

I glossed over this earlier, so let me explain the mechanics of how an LLM actually "calls" a tool.

Modern LLMs (GPT-4, Claude, Gemini, and others) have been trained to produce structured tool calls as part of their output. When the LLM decides it needs to use a tool, instead of producing normal text, it produces a special structured output like:

{
  "tool": "search_web",
  "arguments": {
    "query": "best restaurants in Goa near Baga Beach"
  }
}

The agent runtime — the software layer running the agent — detects this structured output, executes the actual function (in this case, performing a web search), and sends the result back to the LLM as a new message:

{
  "tool_result": "search_web",
  "output": "1. Britto's - Baga Beach, seafood, avg ₹800... 2. Tito's Lane Café..."
}

The LLM then reads this result and decides what to do next — maybe call another tool, or generate a final response.

This back-and-forth is orchestrated by the agent runtime. The LLM itself is stateless — it does not "remember" calling the tool. The runtime manages the full conversation history, injecting tool results at the right points.

This is an important architectural detail: the LLM is the brain, but the runtime is the body. The LLM decides what to do; the runtime actually does it.

Planning: The Difference Between Good and Bad Agents

Not all agents are equally capable, and the biggest differentiator is planning ability.

A naive agent takes things one step at a time. It does the first obvious thing, sees what happens, and then figures out the next step. This works for simple tasks but falls apart for complex ones.

A good agent plans ahead. Before taking action, it thinks about the full task, breaks it into subtasks, identifies dependencies, and creates a rough roadmap. Then it executes against that plan, adapting as needed.

There are several planning strategies that agent systems use:

Plan-and-Execute

The agent first creates a complete plan, then executes it step by step. If something goes wrong, it revises the plan. This works well for well-defined tasks. The agent starts with a rough plan, executes the first few steps, and then refines the plan based on what it learns. This works better for exploratory tasks where the full scope is not clear upfront.

Hierarchical Planning

The agent breaks the task into high-level goals, then breaks each goal into sub-goals, then breaks sub-goals into concrete actions. This is how humans naturally approach complex problems — you do not plan every individual keystroke when writing an essay; you think about sections, then paragraphs, then sentences.

Reflection and Self-Critique

After completing a task (or a sub-task), the agent reviews its own work and asks itself: "Did I do this correctly? Did I miss anything? Is there a better approach?" This self-reflection step catches a lot of errors that would otherwise slip through.

Planning is the hardest part of building good agents. An LLM can be brilliant at individual tasks but terrible at coordinating a complex multi-step workflow. This is an active area of research.

Multi-Agent Systems: Agents Working Together

Once you have one agent working, the next natural question is: what if you had several agents working together?

Multi-agent systems are exactly what they sound like — multiple agents collaborating on a task, each with different roles, tools, or specializations.

Here are some common patterns:

Supervisor Pattern

One "manager" agent coordinates multiple "worker" agents. The manager breaks the task into pieces, assigns each piece to a worker, collects results, and synthesizes the final output. Think of it like a project manager delegating to a team.

Pipeline Pattern

Agents are arranged in a sequence, like an assembly line. Agent A does research, passes results to Agent B who writes a draft, Agent B passes the draft to Agent C who reviews and edits. Each agent specializes in one part of the workflow.

Debate Pattern

Multiple agents tackle the same problem independently, then compare their answers. A final agent evaluates the different approaches and picks the best one (or synthesizes a combined answer). This is useful for tasks where quality matters more than speed.

Swarm Pattern

A large number of simple agents each handle a small piece of a large task in parallel. Think of a hundred agents each summarizing one page of a thousand-page document simultaneously.

Multi-agent systems are powerful but add a lot of complexity. Coordination overhead, conflicting actions, and compounding errors are real challenges. Most practical applications today use single agents or small teams of 2-3 specialized agents.

Real-World Examples: Agents in Action

Let me ground all of this theory with some concrete examples of how agents are being used today.

Coding Assistants

Tools like GitHub Copilot, Cursor, and various coding agents can read your codebase, understand the architecture, write new features, fix bugs, run tests, and iterate until everything passes. They use file-reading tools, code execution tools, and terminal commands.

Research Agents

Given a research question, these agents search the web, read papers and articles, synthesize findings, identify gaps, and produce comprehensive reports. They might make dozens of web searches and read hundreds of pages of content to produce a single report.

Customer Support Agents

These agents handle customer queries by looking up order information in databases, checking inventory systems, processing refunds through payment APIs, and sending confirmation emails — all automatically, with a human in the loop only for escalations.

Data Analysis Agents

Given a dataset, these agents write Python code to clean the data, perform statistical analysis, generate visualizations, and write up findings. They iterate — if a chart does not look right, they modify the code and regenerate it.

Personal Assistants

Agents that manage your calendar, draft emails, book travel, and coordinate schedules by integrating with multiple APIs and services.

The Failure Modes: When Agents Go Wrong

Agents are not magic. They fail, and understanding how they fail is just as important as understanding how they work.

Looping

The agent gets stuck in a loop — trying the same thing over and over without making progress. "Search for X. No results. Search for X. No results. Search for X..." Good agent systems have loop detection and maximum iteration limits.

Hallucinating Actions

The LLM generates a tool call for a tool that does not exist, or passes arguments that do not make sense. The runtime catches these, but the agent might waste several steps before recovering.

Compounding Errors

Each step in the agent loop has some probability of error. Over many steps, these errors compound. A 5% error rate per step becomes a 40% chance of at least one error over 10 steps. This is why longer agent tasks are inherently less reliable than shorter ones.

Losing Context

In a long-running task, the context window fills up, and the agent loses track of earlier information. It might repeat work it already did, or forget a constraint that was established early on.

Over-Confidence

The agent completes a task and reports success, but the result is actually wrong. It did not verify its work properly. This is especially dangerous because the user trusts the agent's confident-sounding report.

Cost Explosion

Each step in the agent loop costs tokens (and therefore money, for API users). A poorly designed agent can burn through hundreds of thousands of tokens on a task that should have taken ten thousand. Every tool call adds to the bill.

Guardrails: Keeping Agents Safe

Because agents can take real actions in the real world, safety is a big concern. Here are some common guardrails:

Human-in-the-Loop

The agent proposes actions, but a human must approve them before execution. This is the safest approach but the slowest. Many systems use this for high-stakes actions (deleting files, sending money) while letting low-risk actions (reading files, web searches) happen automatically.

Sandboxing

The agent operates in a restricted environment where it cannot cause serious damage. Code runs in a container. File access is limited to specific directories. Network access is restricted.

Token and Step Limits

The agent has a maximum budget — either in tokens or in number of steps. If it exceeds the budget, it stops and reports what it has done so far.

Action Allowlists

The agent can only use pre-approved tools and can only call them with pre-approved argument patterns. This prevents the agent from doing anything unexpected.

Output Validation

Before presenting results to the user (or taking real-world actions), the output is validated by a separate system — sometimes another LLM acting as a reviewer.

Agent Frameworks: The Building Blocks

If you want to build your own agent, you do not have to start from scratch. There are several frameworks that provide the scaffolding:

LangChain / LangGraph — One of the most popular frameworks. LangGraph specifically focuses on building agents as state machines with cycles and branching.
CrewAI — Focused on multi-agent systems where you define "crews" of agents with different roles.
AutoGen — Microsoft's framework for multi-agent conversations.
Semantic Kernel — Microsoft's SDK for building AI applications and agents.
Vercel AI SDK — For building agent-like experiences in web applications.

These frameworks handle the boring plumbing — managing the conversation loop, executing tool calls, handling errors, managing memory — so you can focus on defining what the agent should do.

But here is a hot take: you do not always need a framework. For simple agents, you can build the loop yourself in about 50 lines of code. Receive input → call LLM → check if LLM wants to use a tool → execute tool → feed result back → repeat. Frameworks are helpful for complex multi-agent systems, but they can be overkill for straightforward use cases.

The Difference Between Agents and Workflows

This is a distinction that a lot of people miss, and it matters.

A workflow is a predetermined sequence of steps. You define the steps in advance: "First, do A. Then do B. If the result of B is X, do C; otherwise, do D." The logic is fixed. The LLM might be used at individual steps, but the overall flow is controlled by code.

An agent determines the sequence of steps dynamically. The LLM decides what to do at each step based on the current state. The flow is not predetermined — it emerges from the agent's reasoning.

In practice, the most effective systems are hybrids. You use workflows for the parts of the process that are well-defined and predictable, and agents for the parts that require judgment and flexibility.

For example, a code review system might use a workflow for the overall process (check out PR → run linters → run tests → generate review), but use an agent for the review step itself, where the LLM needs to read the code, understand the changes, and provide nuanced feedback.

The Honest Bottom Line

AI agents are not science fiction. They are working software systems that you can build and use today. The core idea is straightforward: take an LLM, give it tools, put it in a loop, and let it work through problems step by step.

But they are also not magic. They make mistakes. They get confused. They sometimes waste a lot of time and money doing things the wrong way before stumbling onto the right approach. They need guardrails, and they need human oversight for anything high-stakes.

The best way to think about current AI agents is as junior employees with superhuman speed. They can do a lot of work very quickly, but they need clear instructions, they benefit from supervision, and you should always check their work before it goes out the door.

That said, they are getting better at a startling rate. The agents of today are dramatically more capable than the agents of two years ago. If the trajectory continues — and there is no reason to think it will not — the agents of two years from now will make today's look primitive.

Understanding how they work is not just useful for building them. It helps you use them more effectively, set realistic expectations, and make better decisions about when to trust them and when to double-check.

And that understanding starts with the loop: think, act, observe, repeat.

If this breakdown helped connect some dots for you, I am glad. Agents are one of those topics where the concept is simple but the details run deep. Feel free to reach out if you want to go deeper on any part of this.