Is an AI agent just an LLM with extra steps?

In a sense, yes, but the extra steps are the whole point. The LLM supplies reasoning and language; the agent adds a loop, tools, and memory so that reasoning can observe the real world and act on it. Without the loop, the model can only describe an action in text and never carry it out.

Can an LLM use tools by itself?

No. The model can emit a structured request that names a tool and its arguments, but it cannot execute anything. A surrounding program (the agent framework) has to parse that request, run the real function, and return the result as the model's next input. The model never touches your systems directly.

How does an agent decide when it is done?

The model signals completion by returning a final answer instead of another tool call. The agent framework also enforces external stopping conditions: a maximum number of steps, a time or cost budget, an unrecoverable tool error, or a point where a human must approve an action. A hard iteration cap is what prevents an infinite, costly loop.

Why do agents hallucinate or get stuck in loops?

Hallucination happens when the model invents a result instead of calling a tool to check, which you prevent by forcing retrieval and feeding real tool output back. Looping happens when the model repeats an action without noticing it made no progress, which you prevent with step limits, loop detection, and prompts that make the model verify each result before continuing.

What makes a production agent different from a tutorial agent?

A tutorial agent is the bare loop: model, tools, and a while statement. A production agent adds explicit long-term memory, hardened tools with auth and permission checks, full observability of every step and cost, and governance such as human approval gates and evaluation harnesses. The model is the easy part; the reliable, observable, governed loop around it is the actual engineering.

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What Is an AI Agent, and How Is It Different From an LLM?

An LLM predicts the next token. An agent wraps that model in a loop with tools, memory, and a goal. Here is the difference, explained from first principles.

August 28, 20269 min readStallwart

The short answer

A large language model (LLM) is a next-token predictor: given a sequence of text, it outputs a probability distribution over what comes next, and you sample from it. That is the whole job. An AI agent is an LLM placed inside a loop, given a goal, a set of tools it can call, and some memory of what has happened so far. The agent uses the model to decide what to do next, takes an action in the world, observes the result, and repeats until the goal is met or it gives up.

So the model is the reasoning engine, and the agent is the system built around it. An LLM on its own reads and writes text in a single pass. An agent perceives its situation, decides on an action, acts, then perceives again. That perceive-decide-act loop is what turns a static text predictor into something that can book a flight, fix a failing test, or query a database and summarize the result.

If you remember one thing: the LLM does not "do" anything by itself. It emits text. An agent is the surrounding program that reads that text, runs the tools the text asks for, feeds the results back, and calls the model again.

What an LLM actually is

Under the hood, an LLM is a function. You give it a sequence of tokens (roughly, word fragments) and it returns a score for every possible next token. Formally it models P(next token | all previous tokens). To generate a sentence, you sample one token from that distribution, append it to the input, and run the function again. Text comes out one token at a time, each conditioned on everything before it.

This has two consequences that matter for the rest of this article. First, the model has no memory between separate calls. Everything it "knows" about the current task must be inside the input you hand it (the context window). Close the call, and that working state is gone unless you saved it yourself. Second, the model cannot act. It cannot read a file, hit an API, or run code. It can only produce text that describes an action. Something outside the model has to notice that text and carry the action out.

The model is also frozen. Its weights were fixed at training time, so it knows nothing about events after its training cutoff and nothing about your private data unless you put that information into the prompt. A pure LLM is therefore powerful at language and reasoning but blind to the current world and unable to touch it.

The agent loop: perceive, decide, act

An agent closes both gaps by wrapping the model in a control loop. The loop is plain software, usually a while statement, and each pass through it does the same three things.

The elegance is that the model never leaves its comfort zone. It only ever reads text and writes text. The agent framework does the messy work: it turns the model's text into real function calls, executes them, and turns the results back into text the model can read on the next pass. The model supplies judgment; the loop supplies hands and a clock.

Perceive: assemble the current context. This is the goal, the history of previous steps, and any new observations (the output of the last tool, an error message, the latest user reply). All of it is formatted as text and placed in the model's input.
Decide: call the model once. It reads the context and produces its next move, either a final answer or a request to use a specific tool with specific arguments.
Act: if the model asked for a tool, the loop runs that tool for real (search the web, query a database, execute code), captures the output, and appends it to the history. Then it loops back to perceive.

Tool calling and planning, concretely

Tool calling is the mechanism that lets the loop "act." You describe each tool to the model as a name, a short description, and a schema for its arguments (for example, a get_weather tool that takes a city string). When the model decides a tool is needed, it does not run it. It emits a structured request such as get_weather(city: "Chennai"). The agent framework parses that request, calls the real function, and returns the result to the model as the next observation. Modern models are fine-tuned to produce these calls in a reliable, parseable format, which is why tool calling works consistently enough to build on.

Planning is what happens across many loop iterations. For a hard goal, an agent may first ask the model to break the task into steps, then work through them one tool call at a time, re-checking after each result. Some agents plan the whole sequence up front; others plan one step, observe, and re-plan (a pattern often called reason-and-act, where the model interleaves a short reasoning trace with each action). Re-planning after every observation is usually more robust, because the real world rarely matches the first plan and the model gets to correct course using what it actually saw.

The reason this feels like more than autocomplete is the feedback. A single LLM call is a guess made blind. An agent turns that guess into a hypothesis, tests it by taking an action, reads the result, and revises. Reasoning plus grounding in real observations is what lets an agent handle tasks a one-shot prompt cannot.

When the loop stops, and how it fails

A loop that never ends is a bug, so termination is part of the design, not an afterthought. An agent stops when the model signals the goal is met and returns a final answer, when a step limit or time or cost budget is hit, when a tool returns an unrecoverable error the agent is told to surface, or when it needs a human decision it is not allowed to make alone. Well-built agents treat these limits as first-class: a hard cap on iterations is what stands between you and an infinite, expensive loop.

The failure modes are worth naming, because they are the reason agents are harder to ship than a demo suggests. Understanding them is most of the job.

Looping: the agent repeats the same action, or two actions in a cycle, making no progress. Guard with step limits, loop detection, and prompts that make the model check whether the last action actually changed anything.
Wrong tool or wrong arguments: the model picks a plausible but incorrect tool, or calls the right tool with malformed arguments. Guard with tight tool descriptions, argument validation before execution, and returning clear errors the model can read and retry against.
No grounding (hallucination): the model invents a fact or a result instead of using a tool to check. Guard by forcing retrieval for factual claims, and by feeding real tool output back rather than letting the model assume the outcome.
Compounding errors: a small mistake early gets built on and amplifies over many steps. Guard with checkpoints, verification steps, and keeping tasks short enough that a bad step is caught before it snowballs.
Context overflow: the running history grows past the model's context window and early facts fall out. Guard with summarization of old steps and by storing durable facts in external memory rather than the raw transcript.

From loop to production system

The three-line while loop is enough to understand what an agent is. It is not enough to run one on real work. The gap between a working demo and a system a business can depend on is where most of the engineering lives, and it is almost entirely about the parts that are not the model.

Production agents add layers around the loop. Memory becomes explicit: short-term state in the context window, plus long-term storage (often a vector database for retrieval) so the agent can recall facts across sessions instead of forgetting everything each call. Tools are hardened with authentication, rate limits, timeouts, and permission checks, because a tool that can write to your database or send email is a real action with real consequences. Observability is added so every step, tool call, and cost is logged and can be replayed when something goes wrong. And governance is layered on top: guardrails on what the agent may do, human approval gates for irreversible actions, and evaluation harnesses that test the agent against known cases before it touches production.

This is the shape of the work at Stallwart: intelligence (the model), orchestration (the loop, tools, and memory), and governance (limits, approvals, observability), assembled into something that runs against a real workflow and real data rather than a slide. The model is the easy part. The reliable loop around it is the product.

The short version

An LLM is a next-token predictor that only reads and writes text; it cannot remember across calls or take actions on its own.
An AI agent is an LLM inside a loop with a goal, tools, and memory, running a perceive-decide-act cycle until the goal is met or a limit is hit.
Tool calling means the model emits a structured request for a function; the surrounding framework runs it for real and feeds the result back.
Termination must be designed in: agents stop on a final answer, a step or cost budget, an unrecoverable error, or a required human decision.
Common failure modes are looping, wrong-tool selection, ungrounded hallucination, compounding errors, and context overflow, each with a specific guard.

The short answers

Questions this raises

Is an AI agent just an LLM with extra steps?: In a sense, yes, but the extra steps are the whole point. The LLM supplies reasoning and language; the agent adds a loop, tools, and memory so that reasoning can observe the real world and act on it. Without the loop, the model can only describe an action in text and never carry it out.
Can an LLM use tools by itself?: No. The model can emit a structured request that names a tool and its arguments, but it cannot execute anything. A surrounding program (the agent framework) has to parse that request, run the real function, and return the result as the model's next input. The model never touches your systems directly.
How does an agent decide when it is done?: The model signals completion by returning a final answer instead of another tool call. The agent framework also enforces external stopping conditions: a maximum number of steps, a time or cost budget, an unrecoverable tool error, or a point where a human must approve an action. A hard iteration cap is what prevents an infinite, costly loop.
Why do agents hallucinate or get stuck in loops?: Hallucination happens when the model invents a result instead of calling a tool to check, which you prevent by forcing retrieval and feeding real tool output back. Looping happens when the model repeats an action without noticing it made no progress, which you prevent with step limits, loop detection, and prompts that make the model verify each result before continuing.
What makes a production agent different from a tutorial agent?: A tutorial agent is the bare loop: model, tools, and a while statement. A production agent adds explicit long-term memory, hardened tools with auth and permission checks, full observability of every step and cost, and governance such as human approval gates and evaluation harnesses. The model is the easy part; the reliable, observable, governed loop around it is the actual engineering.

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