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An agentic workflow uses a defined process to coordinate AI models and tools; an autonomous agent decides dynamically what to do next to achieve a goal. The difference is mainly where execution control sits—not whether AI is involved. A workflow can also include an agent loop, so these are often complementary designs rather than mutually exclusive choices.
What is the difference between an agentic workflow and an autonomous agent?
The terms are not used consistently across vendors, so it helps to name the architectural distinction being used. Anthropic defines workflows as systems in which “LLMs and tools are orchestrated through predefined code paths,” contrasting them with agents that dynamically direct their processes and tool use. OpenAI likewise describes agents as systems that use an LLM to manage workflow execution and make decisions, including selecting tools according to the current state and guardrails.
| Question | Agentic workflow | Autonomous agent |
|---|---|---|
| Who determines the execution path? | An orchestrator or code defines the sequence. A step within it may still use an LLM or an agent. | The agent dynamically directs its process and tool use toward the goal. |
| What tasks fit best? | Well-defined processes where explicit order and predictable execution matter. | Open-ended tasks that require planning or dynamic tool selection. |
| How does it adapt? | A step can reason or adapt while the overall route remains structured. | It can adjust its next action based on observations and results. |
| Does a person have to approve each step? | No. A workflow may be autonomous or may include human interaction. | Not necessarily. Autonomy describes how independently the system acts; guardrails and human handoffs are design choices. |
| What is the main trade-off? | Explicit control over the path, with less flexibility if the process changes unexpectedly. | More flexibility, alongside additional cost, latency, reliability, security, and oversight considerations. |
Microsoft uses “agentic workflow” more broadly for workflows that can include an iterative agent loop. In its Azure Logic Apps documentation, it distinguishes autonomous workflows without human interaction from conversational workflows that involve it. That usage does not contradict the architectural distinction above: a workflow can provide the outer structure while an agent makes decisions inside a step.
When should you use a workflow instead of an autonomous agent?
Choose a workflow when the process is well defined
Use an explicit workflow when you can specify the steps in advance and need to control their order. This suits repeatable processes with known triggers, routing, checks, or approvals. Microsoft’s Agent Framework recommends workflows when execution order must be explicit.
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Choose an agent when the next step depends on what it discovers
An agent is a better fit when a task is open-ended and the system needs to plan, select tools, and revise its actions based on intermediate results. OpenAI’s guide describes this kind of dynamic tool selection as part of agent behavior. A model call or chatbot is not automatically an agent: in OpenAI’s framing, the LLM must control workflow execution rather than simply respond within a fixed process.
Combine them when only part of the task needs autonomy
An outer workflow can handle reliable triggers, routing, and approvals while an agent loop handles a step that needs flexible reasoning. Describe the two layers separately: the workflow determines when the agent runs and what constraints apply; the agent determines its next actions within that boundary. This is a practical synthesis of Anthropic’s architecture distinction and Microsoft’s description of workflows containing agent loops.
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What does “autonomous” mean in practice?
Autonomous does not have to mean unrestricted, unsupervised, or continuously running. It means the system can make some decisions about how to pursue its goal rather than following only a fixed script. The scope of that independence is a design choice: you can limit available tools, set conditions for stopping, or return control to a person for review. Microsoft’s autonomous-versus-conversational distinction is about whether the workflow involves human interaction, not whether the system has no safeguards.
What should you consider before giving an agent control?
Dynamic decisions can help with open-ended work, but add operational responsibilities. Anthropic notes that agentic systems can trade increased task performance for latency and cost. Microsoft’s shared-responsibility guidance highlights memory and state, tools and actions, and orchestration as areas to manage. It also identifies a risk from untrusted content that could lead to malicious tool use.
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- Limit tool access: give the system only the tools and permissions it needs, and define guardrails for their use.
- Set handoffs and stop conditions: decide when the system should stop, request approval, or return control rather than continue acting.
- Review consequential actions: add human review where an error could have significant effects.
- Account for state and orchestration: decide what the system remembers and how the surrounding process manages its actions.
- Start with the simplest design that works: Anthropic recommends adding complexity only when it is needed; more agentic behavior can bring additional cost and latency.
How should you choose?
- Can you specify the steps and their order? If yes, begin with a workflow.
- Must the system choose actions based on changing information? If yes, consider an agent for that part of the task.
- Does only one stage need flexible reasoning? Keep the overall process structured and place an agent loop inside that stage.
- Could an incorrect action matter? Bound the tools, set explicit stopping conditions, and add an appropriate handoff or review.
The label alone does not tell you how much control a system has. Ask instead which decisions are fixed in code, which are made dynamically by the agent, and where a person can intervene.
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