# AI agents vs workflows: what is the difference?

> A workflow follows steps you wrote. An agent decides its own steps in a loop. How to choose between them, with patterns, trade-offs and a simple decision checklist.

Source: https://devaiper.com/blog/ai-agents-vs-workflows
Published: 2026-10-08
Topics: Agents, Workflows, Architecture

**Short answer:** In a workflow your code controls the path and the model fills in steps. In an agent the model controls the path, choosing tools in a loop. Use the simplest option that works: a single call, then a workflow, and an agent only when the steps cannot be known in advance.

"Agent" is the most overused word in AI right now. Here is a precise way to tell the difference: **who decides what happens next?**

## The core distinction

> **Diagram:** Workflow versus agent. In a workflow, your code defines the steps, the path is fixed, and it is predictable and cheap. In an agent, the model chooses the next step in a loop, the path varies, and it is flexible but costlier and harder to test.
> Workflow: Your code decides the steps; Fixed path, model fills in steps; Predictable cost and latency; Easy to test and debug. Agent: The model decides the next step; Path varies per task; Handles open-ended problems; Costlier, slower, harder to test.

## Climb the ladder, do not jump

Start at the bottom. Move up only when the lower rung cannot do the job.

> **Diagram:** A ladder of complexity from simplest to most complex: a single LLM call, a prompt chain or workflow, a workflow with routing and parallel steps, and finally a full agent in a tool loop.
> Single LLM call (classify, summarize, extract) → Workflow: fixed steps (chain, route, run in parallel) → Workflow with loops and checks (evaluator, retry, orchestrator-workers) → Agent (model picks tools in a loop until done)
> Every rung up adds cost, latency and ways to fail.

## Five workflow patterns you will actually use

1. **Prompt chaining.** Output of step one feeds step two (draft, then translate, then format).
2. **Routing.** Classify the input, then send it to a specialized prompt or model.
3. **Parallelization.** Run independent steps at once, or run the same task several times and vote.
4. **Orchestrator-workers.** One model splits a task, others do the pieces, then results are combined.
5. **Evaluator-optimizer.** One call produces, another critiques, repeat until good enough.

All five keep **your code** in charge of the structure.

## What an agent looks like

An agent is a [tool-use loop](https://devaiper.com/blog/tool-use-function-calling-explained): call the model, run the tools it asks for, append the results, repeat until it stops asking. Coding agents like [Claude Code](https://devaiper.com/blog/what-is-claude-code) are the clearest example.

```ts
while (true) {
  const response = await callModel(messages, tools);
  if (response.stop_reason !== "tool_use") break; // model decided it is done
  messages.push(...(await runTools(response)));     // model decided what to run
}
```

The loop is trivial. The hard part is everything around it: tool design, [context management](https://devaiper.com/blog/what-is-context-engineering), permissions and stopping conditions.

## Should this be an agent? A four-question check

| Question | Agent only if... |
|---|---|
| **Complexity** | The steps cannot be fully specified in advance |
| **Value** | The outcome justifies higher cost and latency |
| **Viability** | The model is demonstrably capable at this task type |
| **Cost of error** | Mistakes can be caught or rolled back (tests, review, undo) |

If any answer is no, stay at a simpler rung.

## Cost, latency and safety

An agent makes a variable number of model calls, and its context grows with each tool result. Budget for that: set step limits, trim tool output and cache the stable prefix ([prompt caching](https://devaiper.com/blog/prompt-caching-explained)). Give agents the **minimum** permissions, require approval for irreversible actions, and treat tool output as untrusted ([MCP security](https://devaiper.com/blog/mcp-security-risks)).

## A practical rule

Write the workflow first. If you keep adding "unless" branches that the model could handle better, let it decide that one step. Agentic where it helps, deterministic everywhere else.

And test it: you cannot improve what you do not measure. See [LLM evals explained](https://devaiper.com/blog/llm-evals-explained).

## FAQ

### What is an AI agent?

An AI agent is a language model running in a loop with tools: it decides which tool to call, reads the result and repeats until the task is done or it needs a human. Coding agents like Claude Code are a common example.

### What is the difference between an AI agent and a workflow?

In a workflow, developer-written code decides the sequence of steps and the LLM performs individual steps. In an agent, the LLM decides which steps and tools to use next, in a loop, until it considers the task done.

### When should I use an agent instead of a workflow?

When the task is open-ended, the number of steps cannot be predicted, the value justifies higher cost and latency, and mistakes can be caught or undone. If you can write the steps down, write a workflow.

### Are agents more expensive than workflows?

Usually yes. An agent makes a variable number of model calls and carries a growing context, so cost and latency are higher and less predictable.

### What are common workflow patterns?

Prompt chaining, routing, parallelization, orchestrator-workers and evaluator-optimizer loops.