Agent Workflow

What is an agent workflow?

An agent workflow is a structured sequence or graph of steps through which an AI agent performs and coordinates work. The workflow provides predictable stages, while an agent may make decisions inside selected stages.

For example, a support workflow might receive a request, classify it, retrieve customer data, let an agent propose a resolution, request approval when needed, update the ticket, and verify completion.

How agent workflows are structured

A workflow can include:

• sequential steps;
• conditional branches;
• parallel tasks;
• tool calls and external services;
• agent decision points;
• human approvals;
• retries, timeouts, and failure paths;
• state updates and stopping conditions.

The structure may be expressed in application code, a state machine, a workflow engine, or an orchestration framework.

Workflow versus agent loop

An [agent loop](/glossary/agent-loop/) is the repeating cycle in which an agent decides, acts, observes, and updates state. A workflow defines the larger route through the system.

An agent loop can run inside one workflow step. A workflow can also coordinate several agents, deterministic functions, and approvals without giving any single agent control of the whole process.

Workflow versus plan

A workflow is generally reusable across a class of tasks. A plan is created or adapted for a specific goal and current situation.

For example, “research → verify → draft → approve → publish” may be the workflow. The plan identifies which sources to check, which claims need verification, and how the current report will be produced.

Workflow versus multi-agent system

A workflow describes execution structure. A multi-agent system describes multiple agentic participants. One agent can move through a complex workflow, and several agents can cooperate inside one workflow.

Adding multiple model calls does not automatically make a workflow multi-agent.

Reliability and boundaries

Agent workflows should make state transitions explicit. Write actions need idempotency or duplicate protection, failures need safe retry rules, and approval points should be enforced outside the model.

Too much fixed structure can prevent useful adaptation. Too little structure makes execution difficult to observe, test, and control. The design should reserve agent judgment for ambiguous work and use deterministic logic where rules are stable.

Why it matters

Agent workflows turn individual model decisions into repeatable operational processes. They connect goals, tools, state, approvals, and stopping behavior.

A strong workflow makes the happy path clear without ignoring failure paths. See [How AI Agents Work](/how-ai-agents-work/) for the goal-to-action loop that powers agent decisions within a workflow.