Most useful agent work is not a single action. Chasing an overdue invoice means pulling the ledger, finding what is overdue, drafting a message for each client, sending it, and logging the result. That is a multi-step workflow: a sequence of stages where each one feeds the next. Get the structure right and the agent runs the whole thing reliably. Get it wrong and a small slip in step two quietly poisons everything after it.

This guide walks through building a multi-step workflow in five steps, using an overdue-invoice reminder as the running example. It assumes you already understand the basics in how to set up your first AI agent, and it puts the chaining pattern from AI agent architecture patterns explained into practice.

What a multi-step workflow is

A multi-step workflow is a task an agent completes as an ordered chain of stages, where each stage does one job and hands its result to the next. It is the simplest way to compose an agent: linear, legible, and easy to debug because you can inspect the hand-off between any two stages. The opposite, cramming the whole task into one giant instruction, works in a demo and falls apart the moment one part needs to change.

The reason to build in steps is reliability. When a task is broken into named stages, you can test each one, see exactly where a run failed, and fix that stage without touching the rest. A single mega-prompt gives you none of that; when it misbehaves you cannot tell which part went wrong. The five steps below turn a vague goal into a chain you can trust.

1. Define the outcome

Before any stage exists, write down what a finished run looks like in one or two sentences. For the invoice example: "Every client with an invoice more than seven days overdue has received one polite reminder email, and each send is logged." That sentence is the contract for the whole workflow. It names the trigger, the action, and the proof that the job is done.

Why the outcome comes first

Defining the outcome first stops you from designing steps that do not add up to anything. It is the heart of "describe the outcome, not the workflow": the goal is fixed, and the steps exist only to reach it. A clear outcome also gives you the final check. If you cannot describe how you would verify the run succeeded, you do not yet understand the task well enough to automate it. The thinking behind outcome-first design is in describe the outcome, not the workflow.

2. Decompose into steps

Now work backwards from the outcome. The last stage is "log the send". For that, the prior stage must have sent an email, which needs a drafted message, which needs the list of overdue invoices, which needs the raw ledger. Reverse that and you have your chain: fetch the ledger, filter for overdue, draft a message per client, send, log. Each arrow is a hand-off.

One job per step

The discipline here is that each step does exactly one thing and produces one checkable output. "Filter for overdue and draft the messages" is two jobs hiding as one; split it, so a failure in filtering cannot be confused with a failure in drafting. A good test is whether you can name each step's output in a few words: "the list of overdue invoices", "the drafted emails". If the output is fuzzy, the step is doing too much. This is composability in practice, the subject of AI agent composability explained.

3. Sequence the tools

With the steps named, assign a tool to each and make the outputs line up with the inputs. Fetch uses the accounting tool and returns invoice records. Filter is pure logic and returns a shorter list. Draft uses the model and returns message text per client. Send uses the email tool. Log writes to a sheet. The whole chain works only if each stage emits exactly the shape the next stage consumes.

fetch_invoices()        -> all invoice records
filter_overdue(records) -> overdue list (>7 days)
draft_reminder(client)  -> email text, per client
send_email(client, text)-> send result
log_send(result)        -> appended to log

Writing the chain out like this, even in plain pseudocode, surfaces mismatches early. If the filter returns client IDs but the draft step expects full client records, you have found a bug before building anything. The mechanics of giving each stage the right tool are covered in AI agent tool use explained.

4. Add checks between stages

A chain without checks is a chain that fails silently. Between each stage, add a small validation: did fetch return any records, is the overdue list a list and not an error, does each draft actually contain the client name and amount, did the send return success. A check is cheap insurance that catches a bad result at the boundary, before it flows downstream and ruins the run.

What a good check looks like

A useful check is specific and has an action attached. "If the overdue list is empty, stop and report nothing to do" is better than a vague "make sure it worked". The best checks assert what the next stage assumes, so a broken hand-off is caught at exactly the point it breaks. This is also where you decide what counts as success for the whole run, which connects to the evaluation thinking in AI agent evaluation metrics.

5. Handle failure and test

Finally, decide what happens when a check fails, because it will. For each stage, choose one of three responses: retry, useful for a flaky API; skip, if one client failing should not block the others; or stop and escalate to a human, the right call when a step touches money or is ambiguous. Writing these responses down turns a fragile chain into one that degrades gracefully, the kind of recovery discussed in AI agent error handling and rollback.

Test the pieces, then the whole

Test each step alone first, with sample inputs, so you know the unit works. Then run the full chain on safe test data that produces no real side effects, no real emails, no live writes, and confirm the hand-offs and checks behave. Only when the whole sequence runs clean on test data do you point it at live systems. The dedicated walkthrough for that final gate is how to test an agent before going live, and the natural next step is chaining several agents together in how to chain AI agents for complex tasks.

Frequently asked questions

What is a multi-step agent workflow?

A multi-step agent workflow is a task an agent completes in a sequence of stages, where each stage uses a tool or a decision and passes its result to the next. Instead of one big action, the agent fetches data, transforms it, acts on it, and confirms, with checks between the stages.

How do you break a task into agent steps?

Start from the outcome and work backwards. Ask what the final result needs, then what each prior stage must produce to make that possible. Each step should have one clear job, one tool, and a checkable output. If a step is doing two things, split it into two.

Should I add checks between agent steps?

Yes. A check between stages catches a bad result before it flows downstream and corrupts the rest of the run. Validate that each step produced what the next one expects, and decide what happens on failure: retry, skip, or stop and escalate. Checks are what make a chain reliable.

How do I test a multi-step workflow before using it?

Run each step in isolation first with sample inputs, then run the whole chain on safe test data with no real side effects. Confirm the hand-offs work and the checks catch bad inputs. Only point it at live systems once each stage and the full sequence behave as expected.

Do I need to code to build a multi-step agent workflow?

Not on a platform like Gravity. You describe the outcome and the expert who built the agent has already designed the steps, tools, and checks. Understanding how a workflow is structured still helps you describe what you want clearly and judge whether an agent is built to be reliable.

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