Types of AI Agents: Workflow vs Autonomous, and How to Choose

The content of this article was checked against each vendor's official documentation and public materials on October 2, 2026. Product features may change. Check the sources at the end for the latest details.

Different materials classify AI agents in different ways. This article uses three axes that make it easy for decision-makers to tell them apart.

There is more than one way to classify AI agents

The categories of AI agents change depending on what you focus on. Looking at three points, who sets the steps, what triggers the agent and how many agents run, makes product differences easier to grasp.

As examples of triggers, n8n has the Chat Trigger, which runs from chat, and the Schedule Trigger, which runs at a set time. On how many agents run, the OpenAI Agents SDK provides a way for one agent to hand work to another. Anthropic also describes a setup in which a central LLM splits up the work and hands parts to other LLMs. In a footnote, the AI Guidelines for Business from Japan's Ministry of Internal Affairs and Communications (MIC) and Ministry of Economy, Trade and Industry (METI) describe systems in which multiple AI agents make decisions and act as agentic AI.

Of these, the axis that most affects how you delegate work is who sets the steps. The sections below focus on the two types along this axis.

Among the materials checked for this article, there was no common public classification that rates AI agents by level. The AI Guidelines for Business (version 1.2) say that autonomy includes not only a high degree of autonomy but also systems with some degree of it. As a related idea, MIC's explanation of RPA divides automation into three classes: automating routine work, automating some non-routine work in combination with AI, and advanced autonomy that extends to decision-making.

Workflow type: a person sets the steps in advance

Anthropic describes workflows as systems in which LLMs and tools run along predefined paths. For work with set steps, they behave the same way every time, so their strength is predictability.

Anthropic lists the following five common workflow patterns.

• Prompt chaining: split the work into steps in a fixed order and pass each result to the next
• Routing: classify the input and send each type to its own predefined process
• Parallelization: run split-up tasks at the same time, or run the same task several times and compare the results
• Orchestrator-workers: a central LLM splits the work, hands it to other LLMs and combines the results
• Evaluator-optimizer: one LLM produces output, and another evaluates it and has it revised

Anthropic also gives examples of where each fits. Prompt chaining suits writing ad copy and then translating it into another language. Routing suits customer inquiries, sending general questions, refund requests and technical support to separate processes.

Among tools, n8n chains call AI components in a predefined order. A Dify Workflow takes input, processes it from start to finish in one run and returns the result. A Chatflow adds a conversation layer to a Workflow and runs the flow with each message.

Dify calls these flows agentic workflows. The idea is to use AI's abilities inside a fixed flow that has conditions, review checkpoints and fallback paths for failures. AI does the core of the work, but the builder decides how far it can act.

Autonomous type: the AI decides the steps on the spot

Anthropic describes agents as systems in which the LLM decides its own process flow and tool use, and stays in control of how the work is done. It says they suit work where the number of steps needed cannot be known in advance and the path cannot be fixed.

The official n8n documentation distinguishes agents from chains in that agents use a language model to decide which action to take. A Dify Agent is an app in which the model decides how to proceed and uses tools on its own, without you designing a multi-step workflow. You can set a limit on the cycle of thinking and calling tools, and the higher the limit, the more wait time and usage fees rise.

Anthropic names customer support and software development as areas where the autonomous type has proven effective. Both need conversation and action, have clear success criteria and allow results to be reviewed and corrected. In software development, automated tests can check the results. Even so, Anthropic writes that a person still needs to confirm the result meets the overall requirements.

Anthropic lists three principles for building agents: keep the design simple, make the agent's planning steps clearly visible, and put effort into tool descriptions and testing. From the adopting company's side, whether the planning steps are visible affects whether you can trace the cause of a mistake. The OpenAI Agents SDK also has a logging feature (tracing) that makes agent behavior visible for debugging.

The autonomous type also needs care. Anthropic writes that because it acts autonomously, costs rise and errors can compound. It therefore recommends thorough testing in an isolated environment and adding safety mechanisms.

The two types side by side

Real products often combine the two. In n8n, for example, you can place an AI Agent node as one step in a fixed workflow. How the parts work inside is covered in How AI agents work.

Choose by the nature of the work

Which to choose depends on how fixed the steps of the work are. Anthropic recommends trying the simplest form first and adding complexity only when needed. It also writes that in many cases a single AI call, improved with retrieval and examples, is enough.

When the work is hard to fit into these branches, use the following points as a guide too.

• Success criteria: can you define in words what counts as done
• How to check: can people or tests confirm the result is correct
• Undoing: can actions be reversed when something goes wrong
• Cost and time: can you accept the wait time and usage fees if the loop runs long

For work that struggles to meet all four, a workflow type with steps set by a person is safer than an autonomous type. On the other hand, work that meets all four and whose steps cannot be written in advance is a candidate for trying the autonomous type. Even then, do not run it in production right away. Check the results in a low-impact area first, then expand.

The appendix to the AI Guidelines for Business notes that an AI agent may order products or delete files that people did not intend. The more you lean toward the autonomous type, the more you need to decide in advance where human judgment comes in and what permissions the AI gets. The difference between fixed-step automation and AI is also covered in AI agents vs RPA.

Telling types apart on Employee Store

Employee Store is a marketplace where companies can adopt AI agents (AI employees) built by developers. Because any format is accepted, listings include n8n and Dify workflows, in-house agents, agents built with the Claude or OpenAI APIs, agents built with LangChain and more.

There are seven categories: sales support, recruiting and scouting, social media management, customer support, back office, marketing, and development and data. Categories are based on the field of work, not on how the agent is built.

So workflow and autonomous types are mixed within the same category. On the listing page, check whether the steps are fixed, where human approval comes in, and which tools it gets permissions for and how broad those permissions are. After purchase, you can message the seller on the page for each deal. For how the terms differ in scope, see AI agents vs agentic AI.