---
title: "What is an agentic workflow?"
excerpt: "A fixed sequence of LLM steps you design, not a loop the model steers. Less autonomous than an agent — and for most real tasks, more reliable because of it."
definition: "An agentic workflow is a predetermined sequence of LLM calls and tool uses, orchestrated by your code, where the path is fixed in advance — unlike an agent, which decides its own next step at runtime."
date: "2026-07-09"
lastModified: "2026-07-09"
author: "Teo Deleanu"
authorAvatar: "/team/teo.jpg"
tags: ["Agents", "AI Engineering", "Production AI"]
keywords:
  - "what is an agentic workflow"
  - "agentic workflow vs agent"
  - "llm workflow explained"
---

An agentic workflow is a pipeline of LLM steps whose order *you* decide. Extract, then classify, then draft, then validate — each step is an LLM call or a [tool call](/glossary/tool-calling), but your code owns the sequence. The model does the thinking inside each step; it does not choose what comes next.

## Why it matters in production

This is the reliable middle ground between a single prompt and a fully autonomous [agent](/glossary/ai-agent). Because the path is fixed, a workflow is cheaper, faster, easier to debug, and easier to evaluate step by step. Most tasks that look like they need an agent actually have a knowable shape — and for a known shape, a workflow beats an agent on every axis that matters in production.

## The common mistake

Reaching for an agent's autonomy when the task doesn't need it. Every decision you hand the model is a place it can wander, loop, or burn tokens — and most business tasks follow a predictable path you can encode directly. The reverse mistake exists too: forcing a genuinely open-ended task into a rigid workflow it keeps breaking out of. The skill is matching structure to how determined the task really is.

## How we actually use it

We default to a workflow and escalate to an [agent](/glossary/ai-agent) only when the task is genuinely open-ended, following the [narrow-loop playbook](/blog/narrow-loop-playbook) of keeping the model on the shortest leash the job allows. Each step gets its own [evals](/blog/evals-as-ci), which is far easier when the steps are fixed, and clean [context engineering](/glossary/context-engineering) per step keeps the whole pipeline honest.