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Agentic workflow

An agentic workflow is a way of working in which an AI, instead of answering a question once and stopping, takes a goal, breaks it into steps on its own, uses tools, and repeatedly checks and fixes its results until the job is done.

About this term
Level
Basic · Technical terms you often meet in AI news
Fields
Agents
Added
2026-10-08
In plain words It is similar to how a person works on a report: rather than writing it in one go, they make an outline, gather material, write a draft, and reread and revise it. The difference is that an AI can misjudge when to stop and end up going around in circles on the same task.

How it works

Given a goal, the AI first splits the work into several steps. At each step it uses tools such as search, code execution or file writing, then looks at the result to decide its next action. If the result falls short, it reviews its own work, revises the plan and tries again. The degree of autonomy varies, from setups where a person defines the flow in advance and the AI handles only each step, to setups where the AI also decides the order.

The plan → execute → check loopGoal → Make a plan; Make a plan → Execute step; Execute step → Check results; Execute step → Tools(Use); Check results → Make a plan(Retry); Check results → Finished result(Done); Human check → Make a planGoalMake a planBreak into stepsExecute stepCheck resultsToolsSearch · codeexecution · filesHuman checkAt important stepsFinished resultUseRetryDone
The plan → execute → check loop If the check finds something lacking, the process goes back to planning and tries again; once the goal is met, it delivers the result.

Why it matters

A single question and answer is rarely enough to properly finish multi-step work such as research, coding or document writing. By splitting the work into steps and checking and fixing intermediate results, the same model can produce more accurate and more complete results. That is why this approach has become central as companies try to use AI to automate real work. Coding agents and research services are typical examples.

Things to know

The more steps there are, the more time and cost go up, and a small mistake in an early step can grow larger later on. Because tools touch real systems, safeguards are needed, such as running in a sandbox or human-in-the-loop checks where a person confirms each important step. It is also not needed for everything: for simple questions, answering in one go is often faster and more accurate.

Example

If you ask a coding agent to "fix this bug," it finds and reads the relevant code, guesses the cause, edits the code, runs the tests, fixes it again if they fail, repeats this process, and then reports the result. Research-style AI services also follow an agentic workflow: they split a question into subtopics, search several times, then compare and organize the collected material into a report.

Related terms

AI agent Tool use Multi-agent Human-in-the-loop Sandbox Coding agent