# When to build an agent vs a workflow

> Agents are powerful, but most production AI features are better served by a deterministic workflow. Here is how we decide.

URL: https://twigbit.com/en/blog/agent-vs-workflow
Published: 2026-06-03T00:00:00.000Z
Author: twigbit
Tags: Agents, Architecture, LLMs

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"Agent" has become the default answer to every AI problem. It rarely should be.
An agent — a model that plans its own steps and chooses its own tools in a loop
— is the right tool for a narrow set of problems and a liability for the rest.
The first architectural decision in any AI feature is this: does the task need a
workflow or an agent?

## Start with the workflow

A **workflow** is a fixed sequence of steps, some of which call a model. You
decide the path; the model fills in the language-shaped gaps. Classify the
ticket, then route it. Extract the fields, then validate them. Summarize the
document, then store it.

Workflows are predictable, cheap to run, easy to evaluate, and trivial to debug
because every step is visible. For the overwhelming majority of business
features, a workflow is not the compromise — it is the correct design.

## Reach for an agent only when the path is unknown

An **agent** earns its complexity when the sequence of steps genuinely cannot be
known in advance: open-ended research, multi-step debugging, tasks where the
next action depends on what the previous one returned. If you can draw the
flowchart, you do not need an agent — you need that flowchart, implemented as a
workflow.

## A practical test

Ask three questions before reaching for an agent:

- **Can you enumerate the steps?** If yes, build a workflow.
- **Is the cost of a wrong autonomous action high?** If yes, keep a human or a
  deterministic guardrail in the loop.
- **Can you evaluate the output?** Agents without evals drift silently; if you
  cannot measure quality, you cannot operate an agent in production.

## The hybrid that usually wins

The architecture we ship most often is neither pure agent nor pure workflow: a
workflow as the backbone, with a single bounded agentic step where genuine
open-endedness lives, wrapped in strict tool permissions and an eval harness.
You get the flexibility where it matters and the predictability everywhere else.

Choose the simplest architecture the problem allows. You can always add
autonomy later; you can rarely claw back the reliability you gave up to get it.