Seven Steps To Building An Agentic Workflow

Building an AI agentic workflow starts with a plan, not a prompt. Most teams skip that step and go straight to building, so there's no agreed way to pick the right task, decide when something is ready, or know how much to trust it once it's running on its own. A common obstacle we see with teams adopting AI agentic workflows is the lack of a shared process.

This is the process we use at Triumph before we call any agentic workflow ready. It holds up whether your team is five people or fifty, and none of the seven steps require a developer's background. If you want a sense of where your own team stands with AI before you start, our AI Fluency Rubric is a quick way to check.

A Quick Distinction 

It's worth separating two concepts people tend to lump together: chatbots and agents. A chatbot answers what you ask it, one exchange at a time: you send a message, it responds, and if you want more, you ask again.

An agent works differently. You describe what you want done, and it plans the steps, uses whatever tools and information it has access to, and carries the task through to a finished result, coming back to you only when it needs a decision it can't make on its own. 

The seven steps that follow are for building agentic workflows.

Step 1: Re-Think 

Before you pick a task, check which question you’re asking. It's easy to default to “how can AI do what a person currently does here?” 

Ask a different question instead: what problem is this process here to solve, and how could AI solve that? Staying focused on the problem opens up more ways to use AI than replacing the person ever would.

Step 2: Pick the Task 

Choose something repetitive and well understood, tied to a problem you can name clearly. A mistake on the first try should be easy to catch and cheap to fix: a weekly summary, a first draft of routine notes, a report that follows the same format every time. 

Leave out anything involving data your church hasn't cleared for AI use, anything with legal or financial weight, or anything that would reach someone outside your team before a person reviews it. If your team doesn't have a clear answer yet for what data is and isn't approved to enter into an AI tool, our free AI Use Church Policy Template walks through setting that up, including a framework for approving the tools themselves. 

If the real obstacle is hesitation rather than the task, Rock Cast's episode on unblocking church AI adoption breaks down why teams stall: usually risk aversion, perceived cost, discomfort with the tools, or a workload that's already full. Their fix: find the people on staff who are already curious, give them one small, bounded win, and let them bring the next person along.

Step 3: Outline It 

Don’t hand it off to AI just yet. First, you’ll need to develop the criteria, so the agent has tailored instructions. 

Outline it first, roughly the way you'd brief someone new to the role. A workable outline needs three things:

1. The real problem underneath the task, and why it matters.
2. A clear picture of what “done” looks like.
3. One real example: an actual instance of the task, with realistic input and the output you'd want back.

That example will shape the result more than any amount of explaining. It gives the AI something concrete to build toward and gives you something concrete to hold the result up against later.

Step 4: Build With AI 

With the outline written, hand it to an AI assistant and let it produce a first version. This part tends to move faster than people expect, sometimes minutes rather than days. It works best when the agentic workflow has an actual home, a dedicated project or folder where its instructions and files stay together, instead of getting rebuilt from memory in a new conversation each time. 

Step 5: Review and Stress-Test 

Read the first version the way you'd read a colleague's draft, checking it against the outline and the example from Step 2. For anything that isn't obviously right, choose from five next steps: 

1. Verify: Confirm it's right as is.
2. Revise: Fix what's wrong, then use it.
3. Contain: Ship it, but only in a smaller, safer scope.
4. Escalate: Hand it to someone with more context.
5. Stop: Rework the outline before going further.

Picking one of those five, on purpose, is what makes it a review instead of a glance.

Once it holds up, push it harder than the example you built it on. Feed it a messier, more realistic case, because real input is rarely as tidy as a first test, and that's where you find out what breaks.

Step 6: Classify It 

Different agentic workflows deserve different amounts of trust, and how much depends on a few questions:  

1. What data is involved?
2. What happens if the output is wrong?
3. Is that mistake easy to catch and undo?
4. Who's accountable for the result?

A simple three-color system, built on those questions, covers most of what you'll build.

Naming the color matters less than what it does: it makes the safe next move obvious to whoever's running the agentic workflow, without them having to reason through a policy document every time. 

Step 7: Commit 

Decide how it lives from here. Some tasks fit a schedule, running on their own every Monday morning. Others work better invoked on demand, when a specific situation calls for them. Either way, this is the step where something you tried once turns into something your team depends on.

Where This Leaves You

Run these seven steps once on one real task, and you'll have both an agentic workflow worth trusting and a process you can repeat on the next one. 

If you'd like a group to build that first one alongside, our AI Agentic Workflows Cohort walks a small group of churches through one real workflow together with feedback along the way. If you'd rather have someone from our team work through it with you directly, reach out. We're glad to do that, too.