AI Agents Explained: How They Actually Work

ai agents explained diagram showing the observe, decide, act, tool call loop

AI agents explained in one sentence: software where an LLM decides what to do next, based on the result of what it just did, in a loop, instead of following a script you wrote in advance. It picks which tool to call and with what arguments. That loop, and the model’s control over it, is the entire difference between an agent and a regular app that happens to call an LLM.

Most “agent” content either treats it as a synonym for “chatbot with plugins” or skips straight to a framework’s abstractions. Neither tells you what actually changes in your code. Neither explains why agents burn through tokens and time unpredictably, or how to stop one from calling the same broken tool forever. This is the mechanism.

AI Agents Explained: What Makes Something an “Agent”?

Anthropic’s own engineering team draws the line clearly. A workflow is a system “where LLMs and tools are orchestrated through predefined code paths.” An agent is a system where “LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.”

That’s the whole distinction. In a workflow, your code decides what happens next: call the model, check the output, run step B, call the model again. In an agent, the model itself decides. It picks which tool to call, whether to call another one after seeing the result, and when the task is actually finished.

Anthropic describes the mechanism plainly: agents “are typically just LLMs using tools based on environmental feedback in a loop.” Feed the model a task and a set of tools, let it pick one, run the tool, and hand the result back. Let the model decide the next move. Repeat until the task is done or something stops it.

How Does an AI Agent Actually Call a Tool?

Tool calling is a specific, structured feature most modern LLMs support, not a prompt-engineering trick. You describe each tool as a JSON schema — name, description, and the parameters it accepts — and pass that list to the model alongside the conversation. Ollama’s /api/chat endpoint documents the exact shape:

"tools": [

  {

    "type": "function",

    "function": {

      "name": "get_weather",

      "description": "Get the weather in a given city",

      "parameters": {

        "type": "object",

        "properties": {

          "city": { "type": "string", "description": "The city to get the weather for" }

        },

        "required": ["city"]

      }

    }

  }

]

When the model decides a tool is the right next step, it doesn’t call anything itself — it returns a structured request for your code to execute:

"tool_calls": [

  { "function": { "name": "get_weather", "arguments": { "city": "Tokyo" } } }

]

Your application code runs get_weather("Tokyo"), gets the real result, and sends it back to the model as a new message. The model reads that result and decides what to do next — answer the user, or call another tool. The model never touches your systems directly. It only ever asks, in a format your code chooses to honor or not.

What Is the Agent Loop, and Why Does It Need a Stopping Condition?

Every iteration follows the same shape. The model observes the current state, decides on an action, your code executes that action, and the result feeds back in as the new state. Nothing here is magic — it’s a while loop with an LLM call as the decision function.

⚠️ Note: left unchecked, that loop has no natural end. Anthropic’s own guidance is explicit that tasks “often terminate upon completion, but it’s also common to include stopping conditions (such as a maximum number of iterations) to maintain control.” An agent without an iteration cap is a script that decides its own runtime and your API bill along with it.

A stopping condition can be a hard iteration limit, a timeout, a budget on total tokens spent, or a check for a specific “done” signal from a tool’s result. Pick at least one before an agent goes anywhere near production traffic.

AI Agents Explained: Where MCP Fits In

Tool-calling JSON schemas solve how a model requests a tool. They don’t solve where those tool definitions come from, or how an agent connects to tools it doesn’t already know about. That’s the gap the Model Context Protocol fills. It’s a standard way for a server to expose a set of tools to any compatible agent, instead of every application hand-rolling its own tool integrations.

DevToolHub has covered this ground already: which MCP servers are actually worth connecting and what changed when the MCP spec updated how servers authenticate tool calls — worth reading before you wire an agent into a third-party MCP server, since that authentication layer is exactly what stands between “the agent has read access to your files” and “the agent has whatever access the tool author gave it.”

Why AI Agents Get Stuck in Loops (and How to Actually Stop It)

A common agent failure in production isn’t a wrong answer — it’s the same tool call, with the same arguments, repeated forever. A breakdown of this failure mode puts it bluntly: “nothing in its setup tells it that repeating an identical call is pointless.” The model has no built-in sense that a repeated identical result is a dead end.

The obvious fix — an iteration limit — catches it eventually but wastes the entire budget first. Popular agent frameworks default to fairly generous caps: LangGraph’s default is 25 steps, LangChain’s is 15. A better fix is a no-progress guard: hash the (tool, arguments, result) tuple on every call, and halt the moment the same tuple repeats two or three times. That catches a stuck agent in seconds instead of burning through the full step budget first. Keep the iteration limit too — as a backstop, not the primary defense.

Workflow or Agent? Choosing the Right One for the Task

	Workflow	Agent
Who decides the next step	Your code	The model
Predictability	High — same input, same path	Lower — path depends on model decisions
Best for	Tasks with a known, fixed sequence	Open-ended tasks where the steps depend on what’s found along the way
Debugging	Straightforward — trace the code	Harder — trace the model’s reasoning and tool choices
Runaway cost risk	Low	Real — needs iteration limits and guardrails

Most real systems are not purely one or the other. Wrap an agent around one genuinely open-ended subtask, inside code that controls everything else. That’s usually more reliable than making the entire pipeline agentic just because it’s the more interesting architecture.

AI Agents Explained: Common Mistakes Engineers Make

Giving an agent tools with vague success signals. A tool that returns “processing, check back later” instead of an unambiguous status invites the model to call it again immediately, assuming that’s the fix. Design tool outputs so “done” and “not done” are impossible to confuse.

Trusting the iteration limit to catch stuck loops fast. It will catch them — after burning the entire step budget. A no-progress guard on repeated identical tool calls catches the same failure in seconds.

Skipping the tool-permission question entirely. An agent is only as safe as what its tools are allowed to do. Connecting an agent to an MCP server without checking its authentication model hands the agent whatever access that server’s author decided to grant.

Making everything agentic by default. If the sequence of steps is already known and fixed, a workflow is faster, cheaper, and easier to debug than an agent making the same decisions dynamically every time.

Frequently Asked Questions

Q: What is an AI agent in simple terms?
A: Software where an LLM decides what action to take next — including which tool to call — based on the result of its previous action, repeating in a loop until the task is done or a stopping condition is hit.

Q: What is the difference between an AI agent and a chatbot?
A: A chatbot generates a text response to a message. An agent can take actions — calling tools, running code, querying data — and decides which actions to take based on what it learns along the way, not just what to say next.

Q: Do AI agents use MCP?
A: Many do. MCP standardizes how an agent discovers and calls tools exposed by a server, instead of every application building custom tool integrations for every service it connects to.

Q: Why does my AI agent get stuck repeating the same action?
A: Usually because nothing in the tool’s response signals clearly that the call didn’t make progress, so the model tries again. A no-progress guard that halts after the same tool call and result repeat 2-3 times fixes this faster than an iteration limit alone.

Q: Should I build an agent or a workflow?
A: If the steps are known and fixed in advance, build a workflow — it’s more predictable and easier to debug. Reach for an agent when the right sequence of steps genuinely depends on what’s discovered partway through the task.

Quick Summary — AI agents explained, in practice:
– An agent is defined by who decides the next step: the model, not your code — that’s the entire distinction from a workflow
– Tool calling is a structured JSON feature, not a prompt trick: the model requests a function by name and arguments, your code executes it, the result feeds back in
– The agent loop needs an explicit stopping condition — Anthropic’s own guidance recommends a maximum iteration count at minimum
– Agents get stuck looping on the same failed tool call far more often than they get a wrong answer; a no-progress guard on repeated (tool, arguments, result) tuples catches it faster than an iteration cap
– MCP standardizes how agents connect to tools, but doesn’t replace checking what those tools’ auth model actually permits

Before shipping any agent, write down its stopping conditions and its tool permissions on paper first — both are far easier to get right before the loop is running than after.

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