When to Use AI Agents vs Simple LLM Calls

When should a business use an agent instead of a simple LLM call?

Use a single LLM call when the job is one step. Use a fixed workflow, with a model step inside it, when you can list every step before the run starts. Reach for an agent only when the next step depends on what the model finds along the way.

That puts the burden of proof on the agent, not on the boring option. Every vendor pitch this year says "agents". Most of the work we see on a whiteboard is a workflow that needs one smart step, and building it as an agent makes it slower, harder to test and more expensive to run.

What is the difference between a workflow and an agent?

Anthropic draws the line cleanly in its engineering guide Building effective agents, published December 19, 2024. Workflows are "systems where LLMs and tools are orchestrated through predefined code paths." Agents are systems where LLMs "dynamically direct their own processes and tool usage."

Put plainly: in a workflow, your code decides what happens next. In an agent, the model decides.

A checklist is the easiest picture. A workflow is a checklist someone follows: open the document, pull these fields, check them against the customer record, send it to the right queue. An agent is a new hire you send off to "figure out why this account is behind" without telling them where to look first. Both are useful. You would not hand the second one a task the first one can do.

In our experience, most production systems that look like AI are fixed workflows with one or two model steps inside them. The model reads, classifies or drafts. Everything around it, the order of steps, the checks, the hand-off to a person, is plain code. That is not a lesser kind of AI. It is the kind that keeps working on a Tuesday when nobody is watching.

How do you tell which one a task needs?

There is one question that settles most cases: can you write the full step list before the model runs?

If yes, it is a workflow. Write the steps in code and use the model only where judgment is needed. If no, because the path genuinely depends on what turns up halfway through, that is where an agent earns its place.

Anthropic's guide says the same thing from the other side. It describes agents as suited to "open-ended problems where it's difficult or impossible to predict the required number of steps," and says workflows "offer predictability and consistency for well-defined tasks."

Signals that point to an agent:

• The work is exploratory. Research across sources where the next search depends on the last result.
• The number of steps changes from run to run. One case takes two lookups, the next takes twelve.
• Tool choice depends on what the model just learned. It reads a log, decides it needs the database, then decides it needs the ticket history.

Signals that point to a workflow:

• A document always gets the same treatment. Classify it, extract fields, validate them, route it.
• The output has the same shape every time. A filled form, a summary in a fixed template, a yes or no with a reason.
• A person reviews at a known point. The model drafts, a human approves, the system files it.

Here is what that looks like for a typical back office. An intake team gets PDFs by email, needs a few fields pulled out, needs them checked against existing records, and needs a person to approve before anything is saved. Every step is known in advance. That is a workflow with one model step, the extraction, and a human gate at the end.

It is also how we build our own products. The rate confirmation intake in Howdy Dispatch is that exact shape: the dispatcher drops in a broker PDF, a model pre-fills the load, and the dispatcher reviews and saves. No agent is deciding what happens next, and nothing about it would be better if one were.

What does an agent cost compared with a workflow?

Anthropic's guide is direct about the tradeoff: "Agentic systems often trade latency and cost for better task performance." Its advice is to find the simplest solution possible and only increase complexity when needed.

We will not put a dollar figure on that, because it depends entirely on the task, the model and how often it runs. But the costs show up in the same places every time.

• An open-ended number of model calls. A workflow makes the calls you wrote. An agent makes as many as it decides it needs, which is the point and also the bill.
• Harder debugging. When a workflow breaks, you find the step that broke. When an agent goes wrong, you read a transcript of choices and work out which one went sideways.
• Harder evaluation. A fixed output shape is easy to test against a set of known examples. A free-ranging path is not.
• A bigger surface for permission problems. An agent that chooses its own tools needs real limits on which tools, which data and which actions need a human yes.

None of that means agents are bad. It means they come with a reliability budget. When an agent is the right call, most of the work moves into the scaffolding around the model, which is why we argue that agent reliability is a harness problem as much as a model problem. If you choose an agent, budget for that harness from day one.

What are the signs you built an agent where a workflow would do?

These three show up often enough that we look for them first when someone asks us to fix an agent that "mostly works".

It takes the same path on nearly every run. If you read twenty traces and the agent calls the same tools in the same order nineteen times, you have a workflow that is paying agent prices.

You keep adding rules to the prompt to force the order. "Always check the customer record before drafting. Never send before validating." Every one of those lines is a step you could have written as code. A prompt full of ordering rules is a workflow written in prose, and prose is the least reliable place to keep it.

You cannot say what a good run looks like. If nobody can describe the correct output for a given input, nobody can build a test set, and nobody can tell whether last week's change made it better or worse. That is a sign the task was never pinned down, and an agent hides that instead of fixing it.

The fix is usually the same. Pull the fixed steps out of the prompt and into code. Keep the model for the one step that genuinely needs judgment. Add a test set for that step. What is left is cheaper, faster and easier to trust, and if a real open-ended piece remains, it is now small enough to run as an agent with clear limits.

How do we decide this on a real project?

The first step of our method, which we call how we work, is figuring out what is worth building. Agent versus workflow is one of the earliest calls in that step, and we make it on purpose rather than by default.

We start with the thinnest slice that does something useful. Almost always that slice is a workflow: fixed steps, one model call where judgment lives, a person at the point where a wrong answer is expensive. We ship that, watch it on real inputs, and only add agent behavior where the discovery step turns out to be the actual bottleneck.

That order matters for the budget too. It is the same logic we use for build versus buy for the back office: buy the commodity, build the edge, and do not pay for complexity the task does not need. When a client does need an agent, the earlier workflow is not wasted. It becomes the tools and the guardrails the agent works inside.

This is also the call we are happy to make against our own interest. Sometimes the honest answer is "this is three API calls and a review screen", and that is a smaller project than the one on the vendor slide. If you want that built, it is the kind of custom software for AI workflows we do. If you would rather run the first version yourself, we also teach people to use Claude one on one.

FAQ

Do I need an AI agent for my business? Probably not for your first project. Most business tasks have a known sequence of steps, so a workflow with one model step does the job with less cost and less risk.

Is a workflow with an LLM step still AI automation? Yes. The model does the reading, sorting or drafting, and code handles the order and the checks. That is real automation, and it is the version that tends to survive contact with production.

Are agents more accurate than workflows? Not by default, and they are harder to check. Anthropic notes that agentic systems often trade latency and cost for better task performance, which only pays off when the task truly needs open-ended steps.

Can I start with a workflow and move to an agent later? Yes, and it is usually the best path. The workflow gives you tested steps and a clear definition of a good result. If an open-ended piece remains, an agent can be added on top with those steps as its tools.

Not sure which one you need?

Tell us the workflow you have in mind and we will tell you, plainly, whether it needs an agent. Start the conversation.