Glossary · Agents and autonomy
Workflow
A workflow is an organized sequence of steps for completing a task, including the rules and approvals that connect them. In an AI tool, it describes how information moves between people, models and services, while allowing some steps to involve judgment or flexible decisions.
Workflow: the explanation
A workflow specifies what starts the work, which operations follow and what conditions allow progress. One step might receive a document, another extract information and a later step ask you to approve a result. Branches handle different situations. When a required attachment is missing, the process can pause or request it. Information produced at one stage becomes input for another, according to the relationships and rules configured for that particular process in the application.
The idea comes from organizing work and exists independently of artificial intelligence. It helps make a process repeatable and understandable. In applications using language models, a workflow can provide structure around variable tasks such as categorizing requests or drafting replies. Conventional software, a model and a person may each handle different stages. The term therefore describes an arrangement of work rather than a particular technology, product category or guaranteed degree of autonomy for the system.
A workflow can include decisions made by an agent while keeping the overall procedure predefined. It can also operate without an AI model. Its usefulness depends on how it handles exceptions, failures and restarts. An attractive diagram may still leave important situations uncovered. You need to understand what happens when information is missing, a service stops responding or two stages produce conflicting results, especially when the process performs actions that are difficult to repeat safely.
A concrete example
You manage registrations for a local workshop. A workflow receives each form, checks required fields and uses an AI model to suggest a category. For complete applications, it prepares a reply for you to review. For incomplete ones, it follows a rule to request the missing document. Each application keeps a visible status, so you can resume an interrupted case without starting over.
Why it matters
A workflow helps you see where AI contributes and where your decision is required. When comparing tools, examine which stages you can change, what controls progression and how failures are handled. A process that reflects your real cases makes work easier to track. An overly rigid one can create repeated exceptions and leave you spending more time on manual corrections than you expected.
Common misconceptions
- A workflow does not necessarily automate an entire task, because it can include human work, pauses and approvals that organize the process while preserving your involvement in important decisions and unusual cases along the way.
- Adding an AI model to one stage does not make the whole workflow autonomous, because its triggers, permitted routes and completion conditions can still be explicitly defined and enforced by conventional software outside the model.
Related terms: agents and autonomy
- AI agent
- An AI agent is a system that uses a model to decide which steps to take toward a task.
- autonomous agent
- An autonomous agent is an AI agent that can pursue a goal and choose several actions without involving you at every step.
- multi-agent system
- A multi-agent system is a collection of agents that divide tasks or exchange information to pursue an objective.
- planning (AI agents)
- Planning in AI agents is the preparation of steps an agent intends to follow toward a goal.
- agent memory
- Agent memory is the set of mechanisms that lets an AI agent retain and retrieve information useful to its work.
- human in the loop
- Human in the loop is an arrangement in which a person participates at selected stages of an AI system’s work.
- tool calling
- Tool calling is a mechanism through which a model asks software to run a specific function, such as searching documents or checking a calendar.
- computer use
- Computer use is a capability that lets an AI agent interact with a graphical interface by clicking, typing or scrolling.
Where this site covers it
The term appears on 207 pages of this site. A few of them:
- n8n and AI: A complete guide to nodes, agents, memory, hosting, and costs Guide
- LangGraph reference guide: state graphs, memory, tools, and deployment Guide
- Computer use agents: how they work, available tools, reliability, and security Guide
- AI automation in Make and Zapier: features, agents, limits, and pricing Guide
- CrewAI reference guide: agent teams, tasks, tools, flows, and deployment Guide
- Reduce AI agent costs: model selection, caching, context, routing, and budgets Guide
- Getting started with an AI agent: choosing tools, setup, first tasks, and safeguards Guide
- Monitoring an AI agent in production: logs, traces, alerts, and observability tools Guide
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Frequently asked questions
Do you need to write code?
Not always. Some tools offer visual configuration. You still need to understand the information being exchanged, the conditions for moving forward and the consequences of errors, even when setting up the process requires no programming.
Can it involve several agents?
Yes. A workflow can assign different stages to several agents and combine their results. It then needs clear responsibilities, rules for sharing information and a way to handle disagreements, missing outputs or failed tasks.
What happens after an interruption?
A workflow may save its state and resume at a defined stage. Check how your tool prevents duplicate actions, especially when a message was sent successfully but the confirmation was never recorded by the system.
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