# Agentic workflows vs. AI agents: when to use each

> A workflow follows steps you define, an agent decides its own steps. How they differ, when to use each, and how to build both in Agno.

- Published: 2026-10-22
- Topics: Concepts, Agno SDK
- Canonical: https://www.agno.com/kb/guide/agentic-workflows-vs-ai-agents
- Markdown: https://www.agno.com/kb/guide/agentic-workflows-vs-ai-agents.md

An agentic workflow runs a sequence of steps that you define in code, with a language model handling some of those steps. An AI agent gets a goal and a set of tools and decides for itself which steps to take and in what order. The main difference is whether your code or the model controls the path. Some sources use "agentic workflow" for any system built with agents; this guide uses the narrower meaning: a fixed, code-defined process with model-powered steps.

Agents and workflows can be combined in the same system. Workflows handle the parts of a job that should run the same way every time, and agents handle the parts where the right next step depends on what the model finds along the way. Teams often run into trouble by using one approach for everything, usually an agent, since agents make the better demo.

![Who decides the path. Agentic workflow: your code sets the path, running extract fields, match to PO, approve if over 10,000 dollars, and post to ledger in the same order every run. AI agent: the model sets the path, deciding the next step, calling a tool, and looking at the result, repeating until the goal is met.](https://www.agno.com/images/workflow-vs-agent-path.png)

## What is an agentic workflow?

An agentic workflow is a process with a fixed structure, written in code, where some of the steps use a language model. The order of the steps, the branches, and the conditions for moving from one step to the next are all decided by the developer ahead of time.

Take invoice processing. A workflow might extract the fields from an uploaded PDF with a model, check the totals against the purchase order in code, send anything over $10,000 to a person for approval, and post the result to the ledger. The model reads the invoice, but it doesn't decide whether to check the purchase order or skip approval. Those rules live in the workflow.

![The invoice workflow. A model extracts the fields, your code matches them to the purchase order, and your code always checks whether the total is over 10,000 dollars. If yes, a person approves before the invoice is posted to the ledger; if no, it goes straight to the ledger.](https://www.agno.com/images/invoice-workflow.png)

Because every invoice follows the same predefined steps, the process is predictable and easy to audit. It can only handle the paths you wrote, though, so an invoice that doesn't fit them needs a person or a new branch.

## What is an AI agent?

An AI agent is a language model that works toward a goal by deciding its own next step, calling tools, looking at the results, and repeating until it's done. The developer supplies the instructions and tools, and the model chooses how to use them.

Now take a vendor who emails, "Why hasn't invoice 4471 been paid?" There's no fixed path to the answer. The agent might look up the invoice, see it's waiting on approval, check who the approver is, find they're out of office, and draft a reply explaining the delay. For a different question, it would take different steps. Writing all of those possible paths into a workflow ahead of time isn't practical.

![One vendor question, the agent's own path. The agent looks up invoice 4471, sees it's waiting on approval, checks the approver, finds the approver is out of office, and drafts a reply. The model chose each step; a different question takes a different path.](https://www.agno.com/images/vendor-question-agent-path.png)

Agents are good at this kind of task, but their path can change from run to run, and it's harder to guarantee they'll always perform a required step. They also tend to use more model calls than a workflow doing similar work. Anthropic's guide, [Building effective agents](https://www.anthropic.com/engineering/building-effective-agents), draws the same line: workflows run through predefined code paths, while agents direct their own process and tool use.

## How do agentic workflows and AI agents differ?

Agentic workflows and AI agents differ in who decides the path. In a workflow, your code decides; in an agent, the model decides.

|                           | Agentic workflow                      | AI agent                                                          |
| ------------------------- | ------------------------------------- | ----------------------------------------------------------------- |
| Who decides the next step | Your code                             | The model                                                         |
| How the path is set       | Defined ahead of time                 | Chosen by the model each run                                      |
| Required steps            | Enforced by code-defined control flow | Chosen by the model; required checks can be enforced in tool code |
| Handles unexpected cases  | Only the paths you wrote              | Adapts to new situations                                          |
| Cost and latency          | More predictable                      | Varies with how many steps the model takes                        |
| Testing                   | Test each step and branch             | Test outcomes across many runs                                    |
| Good fit                  | Repeatable processes with clear rules | Open-ended tasks where the steps aren't known ahead of time       |

In practice the line is less sharp than the table suggests. A workflow can include an agent as one of its steps, and an agent can be given a workflow as one of its tools, so the real decision is which parts of a job need a fixed path. [The 5 levels of agentic software](https://www.agno.com/blog/the-5-levels-of-agentic-software-a-progressive-framework-for-building-reliable-ai-agents) covers how systems grow from a single agent into something larger.

## When should you use a workflow, an agent, or both?

Use a workflow when you know the steps and they must happen the same way every time. Use an agent when the steps depend on what the model finds. When a job has both kinds of work, put the agent inside the workflow for the part that needs judgment.

A workflow is the better choice when:

- Skipping a step would cause real damage, such as an approval, a compliance check, or a payment.
- You need to explain afterward exactly what happened and why.
- The same process runs thousands of times and cost needs to be predictable.

An agent is the better choice when:

- The request is open-ended and the path to an answer varies, as in research, troubleshooting, or answering customer questions.
- Listing every path ahead of time would take longer than letting the model work it out.

The invoice example uses both. The workflow fixes the order and the approval rule, and a model handles the one step that needs judgment: reading a PDF that could come in any layout.

Vendor questions go to a separate agent, because every question needs a different set of lookups. That agent can call the workflow as a tool (Agno's `WorkflowTools`) when a vendor resubmits an invoice, so the same checks still apply.

![Both, in one system. The vendor-questions agent decides its own lookups and calls the invoice workflow as a tool through WorkflowTools. The workflow runs extract, match, approve, and post with the same checks every time. AgentOS runs both in one service with the same auth, sessions, and tracing.](https://www.agno.com/images/workflow-and-agent-together.png)

If you're not sure where to start, start with a workflow and add an agent step where the fixed path breaks down. [One agent is all you need (until it isn't)](https://www.agno.com/blog/one-agent-is-all-you-need-until-it-isnt) covers the other direction, when a single agent needs to become a team.

## How do you build an agentic workflow in Agno?

Agno has both as first-class building blocks: `Agent` for model-driven work and `Workflow` for a defined sequence of steps. A workflow step can be an agent, a team of agents, a plain Python function, or another workflow, and the sequence can include branches (`Condition`, `Router`), loops (`Loop`), and parallel groups (`Parallel`).

Here's the invoice workflow from the diagram. The extraction step uses an Agno `Agent` without tools to read the invoice. The purchase-order check, approval, and ledger posting are your own functions:

```py
from agno.agent import Agent
from agno.workflow import Condition, Step, StepInput, Workflow

# Your own functions for the deterministic steps
from app.finance import check_purchase_order, invoice_total, post_to_ledger, request_approval

extractor = Agent(
    name="Invoice Extractor",
    instructions="Extract the vendor, invoice number, PO number, and total from the invoice.",
)

def needs_approval(step_input: StepInput) -> bool:
    return invoice_total(step_input) > 10_000

invoice_workflow = Workflow(
    name="Invoice Processing",
    steps=[
        extractor,  # model step (an Agent with no tools)
        Step(name="Match to PO", executor=check_purchase_order),
        Condition(
            name="Approval check",
            evaluator=needs_approval,
            steps=[Step(name="Person approves", executor=request_approval)],
        ),
        Step(name="Post to ledger", executor=post_to_ledger),
    ],
)
```

This example shows the workflow structure. Your application supplies the invoice input, validates the extracted fields, and implements the purchase-order check and ledger posting.

The Condition is ordinary Python, so the $10,000 check runs on every invoice and the model can't skip it. It still relies on the extracted total, which is why the workflow matches that total against the purchase order first. The extraction step's output can still vary between runs, while the steps around it follow the same predefined rules each time.

AgentOS serves workflows and agents side by side, so the invoice workflow and the vendor-questions agent can run in the same service behind the same auth, sessions, and tracing. The [workflow docs](https://docs.agno.com/workflows/overview) and [workflow patterns](https://docs.agno.com/workflows/workflow-patterns/overview) cover each building block in detail.

## How do you build an AI agent in Agno?

Here's an agent that answers vendor questions about unpaid invoices. It can look up an invoice and check the approver's availability, choosing which tool to call based on what it finds. The tools are your own Python functions:

```py
from agno.agent import Agent

# Your own typed, documented functions connected to your systems
from app.finance import lookup_invoice, get_approver_availability

vendor_agent = Agent(
    name="Vendor Questions",
    instructions=(
        "Answer vendor questions using the available tools. "
        "Explain the payment status using the records you find. "
        "If information is missing, say so."
    ),
    tools=[lookup_invoice, get_approver_availability],
)

vendor_agent.print_response("Why hasn't invoice 4471 been paid?")
```

The model chooses which tools to call and in what order. If the invoice is waiting on approval, it might check the approver's availability. If the invoice has already been paid, it can answer from the payment record. Unlike the workflow above, the code does not prescribe that sequence.

This example uses Agno's default OpenAI model and requires the `openai` package and `OPENAI_API_KEY`. The functions imported from `app.finance` are application code, not built-in Agno tools.

## Sources

- [Anthropic: Building effective agents](https://www.anthropic.com/engineering/building-effective-agents)
- [Agno docs: What are Workflows?](https://docs.agno.com/workflows/overview)
- [Agno docs: Workflow patterns](https://docs.agno.com/workflows/workflow-patterns/overview)
- [Agno docs: Conditional workflows](https://docs.agno.com/workflows/workflow-patterns/conditional-workflow)
- [Agno: AgentOS](https://www.agno.com/products/runtime)

## Frequently asked questions

### What is the difference between an agentic workflow and an AI agent?

In an agentic workflow, your code fixes the order of steps and a language model handles some of them; in an AI agent, the model chooses its own steps and tools as it goes. Agents and workflows can be combined in the same system: a workflow for the steps that must run the same way every time, and an agent for the parts that need judgment.

### What is an agentic workflow?

It’s a process where the developer defines the steps, branches, and rules in code, and a language model does specific jobs inside it, such as reading documents, classifying requests, or writing text. Every run follows the same predefined rules, though different inputs can take different branches. This makes the process easier to audit and its cost easier to predict.

### What are examples of agentic workflows?

Common examples include invoice processing, where a model extracts fields and code checks totals and routes approvals; support ticket triage, where a model classifies each ticket and rules send it to the right queue; lead enrichment, where a model summarizes research that a fixed pipeline collects; and content pipelines that research, draft, and review in a set order.

### When should I use a workflow instead of an agent?

Use a workflow when the steps are known, some of them must always happen, such as approvals or compliance checks, and you need predictable cost and a clear record of what ran. Use an agent when the task is open-ended and the right next step depends on what the model finds.

### Can a workflow include an agent?

Yes, and that’s a common pattern. The workflow fixes the overall order and rules, and an agent handles a step that needs judgment and tools, such as looking up a vendor’s payment history before approval. A tool-free Agent can also handle a single model call, like extracting invoice fields. In Agno, you can pass an agent directly as a workflow step.

### Are agentic workflows more reliable than agents?

Workflows make execution more predictable by defining the order and conditions in code. Agents choose their next steps dynamically, but their tools can still enforce required checks and approvals. Both need testing because model outputs can be wrong. [Agent evals are the new unit tests](https://www.agno.com/blog/agent-evals-are-the-new-unit-tests) shows how to check both kinds of behavior on every change.

### Does Agno support both workflows and agents?

Yes. Agno provides `Agent` and `Workflow` as building blocks, and workflows can include agents, teams, Python functions, and nested workflows as steps, with branches, loops, and parallel groups. AgentOS serves agents and workflows together in one runtime.