Agentic Workflows
If an agent is an individual worker, an agentic workflow is a team with a game plan. Instead of one agent doing everything, workflows define stages — planning, execution, verification — with built-in checkpoints and retry logic. When something fails, the workflow doesn't just stop; it goes back, adjusts, and tries again.
Prerequisites
Before reading this page, make sure you understand:
How It Works
The diagram shows the plan-execute-verify pipeline — the structure that turns individual agent capabilities into reliable, repeatable systems.
In the plan phase, a planner agent breaks the task into discrete steps. If the task is "write a project README," the planner might produce: (1) research the project structure, (2) write a draft, (3) review and edit. This decomposition is critical — it turns an ambiguous goal into a concrete sequence.
In the execute phase, an executor agent carries out each step. It has access to tools — file I/O, code execution, API calls — and works through the plan one step at a time. Each step produces output: a file written, a section drafted, a test result.
In the verify phase, a verifier agent checks whether the output meets quality criteria. Does the README include all required sections? Is the formatting consistent? Are the links valid? The verifier evaluates the work objectively.
If verification fails, the workflow loops back to the planner with feedback on what went wrong. The planner revises its approach, the executor tries again, and the verifier checks the new output. This retry loop continues until the output passes or a retry limit is reached.
This is exactly how structured workflow systems operate: a planning phase produces structured plans, an execution phase carries them out, and a verification phase ensures quality — with automatic retries when things don't meet the criteria.
Why It Matters
Individual agents can get stuck in loops or drift off target. Workflows add structure: separation of concerns (plan vs. execute vs. verify), error recovery through retry logic, and quality gates that catch problems before they compound.
This is the difference between "AI that tries" and "AI that delivers." A lone agent might write a function that doesn't compile and move on. A workflow catches the compilation error, feeds it back, and gets a working version — all without human intervention.
Production AI systems almost always use agentic workflows rather than standalone agents. The added structure makes them predictable, debuggable, and reliable enough for real work.