LLM Agent: What It Is and How It Differs From an LLM

An LLM agent is a language model that has been given tools and a loop. The model decides what to do, something runs it, the result comes back, and the model decides again — repeating until the task is done.

That loop is the entire difference. A large language model on its own produces text. An agent produces actions, checks what happened, and adjusts.

Three things follow from that, and they're the whole article:

• An agent can do things a model can't, because it reaches outside itself — searching, querying, writing files, calling APIs.
• An agent can fail in ways a model can't, because a wrong step becomes the input to the next step.
• Most tasks don't need one. If you can write the steps down in advance, a fixed script is cheaper, faster, and easier to debug.

Agent vs LLM: the actual difference

Ask a model to "find our three biggest accounts and draft a check-in email." A plain LLM writes a plausible email with invented account names, because inventing them is the only thing it can do — it has no access to your accounts.

An agent queries the database, gets three real names back, and drafts the email from those.

Same model, same prompt. The difference is that the agent was handed a tool and permission to use it before answering.

The last row is the one people underestimate. A single model call has a knowable cost. An agent decides how many calls it needs, which means the cost of a task isn't fixed until the task ends.

What an agent is made of

Four parts, and none of them is exotic.

The model. The decision-maker. It reads the situation and picks the next action.

Tools. Functions the model can call — a search, a database query, a file write, an API request. Anthropic's tool use documentation covers the mechanics: you describe each tool, and the model returns a structured request to call one.

Memory or state. What the agent has done so far, carried into the next turn. Without it there's no loop, just a series of unrelated calls.

A stopping rule. An iteration cap, a budget, or a completion condition. Agents that lack one don't finish — they spin.

The tool descriptions matter more than anything else on that list. They're the agent's entire understanding of what it can do. A vague description is the single most common cause of an agent picking the wrong tool, and it looks like a model problem when it's really a documentation problem.

Where agents earn their cost

The honest test is whether the next step depends on what the last step found.

Good fits:

• Research and synthesis. You don't know how many sources you'll need until you start reading them.
• Triage. Classify the thing, then take a different action depending on the class.
• Code changes. Run the tests, read the failure, fix, run again. The feedback is machine-checkable, which is why coding agents work better than most other categories.
• Recovery from messy input. A document that's sometimes a PDF and sometimes a scan needs a decision, not a rule.

Bad fits:

• Anything you could write as a checklist. Fixed pipelines are cheaper and testable.
• High-volume, identical, structured work. Use code.
• Tasks where a wrong action is expensive and can't be undone.

Anthropic's guidance on building effective agents makes the same argument from the builder's side: start with the simplest thing that works, and add the loop only when the task genuinely needs it. Most production systems that call themselves agents are closer to agentic workflows — fixed sequences with a model in one or two of the steps.

How agents go wrong

Looping. The agent calls a tool, dislikes the result, and calls it again with nearly the same argument. Almost always a tool problem: what came back didn't match what the description promised.

Context bloat. Every observation stays in the conversation. Twenty steps in, most of the window is stale intermediate results and the model starts losing the thread.

Too many tools. Past roughly a dozen, selection accuracy drops noticeably. Group them behind a router rather than presenting thirty at once.

Silent wrongness. The worst one. The agent completes the task, reports success, and the output is wrong in a way nothing checked. This is why tasks with machine-verifiable results — tests pass, the schema validates, the number reconciles — are where agents perform best.

Log every action. The log makes the cause obvious in a way the final output never does.

Running an agent without building one

Most people who want an agent don't want to build one. They want a specific working agent that someone else already got right.

That's where the practical wall sits. The good ones live in repositories with dependency lists, environment variables, and API key setup — and reproducing somebody's environment takes longer than understanding their idea did. It's the same gap behind why most AI tools end up in your bookmarks.

Taku is an AI-native desktop workspace built for that gap: mirror an agent or workflow someone already got working, run it on your own files, and remix it rather than rebuilding it. The free app library is the fastest way to see what that looks like. Taku is in Beta, and the Mac app is available now.

FAQ

What is an LLM agent?

A language model connected to tools and run in a loop, so it can take actions, observe the results, and decide what to do next. The model supplies the judgment; the tools supply the reach.

What's the difference between an agent and an LLM?

An LLM maps text to text in a single pass. An agent wraps a model in a loop with tools, so it can gather information it didn't start with and act on the world rather than only describing it. Every agent contains an LLM; most LLM usage isn't agentic.

Do I need an agent framework, or can I write the loop myself?

The loop is genuinely simple — call the model, check for tool requests, run them, append the results, repeat. Writing it once is a good way to understand it. Frameworks like LangGraph earn their place when you need streaming, checkpointing, human-in-the-loop interrupts, and persistence, which is where the fiddly parts live.

How much does an LLM agent cost to run?

More than a single call and less predictably, because the agent decides how many steps it takes. Set an iteration cap and a spend limit before you run anything against real volume — retries and error loops are what turn a cheap task into an expensive one, and they never show up in a demo.

Are LLM agents reliable enough for production work?

For narrow tasks with checkable output, yes, and plenty of teams run them that way. For open-ended work where nothing verifies the result, treat the output as a draft that a person reviews. The reliability question is really a verification question: if you can't check the answer automatically, someone has to.