Agentic Engineering (AI Workflow Engineering)

Which task should you start with?

Start with one task you know well. Pick work you repeat often, that takes effort to coordinate, and that is safe to test with examples you approve. Keep important decisions, unusual cases, and judgment with the people responsible for them.

Hypothetical example: sorting requests

Imagine a business that receives the same kind of request often. A workflow might sort each message into a queue and suggest a reply. A person checks the category and reply before anything is sent. To scope it, we would agree which messages it may read, what it may suggest, who reviews the result, and how to pause access. This example is made up. It is not client work or a promise that AI is a fit.

Write down the rules first.

We agree on the task and what information the workflow can use. We name where information will go, what the workflow can do, and when it needs approval. Before we connect anything, we check what each service may keep and how to remove its access.

We map the steps: gather approved information, make choices within your written rules, take agreed actions, and report what happened. Then we test each step. Tests include missing information, wrong answers, and cases that should go to a person. We agree what a good result looks like and who checks it.

Keep a person in charge.

A person should be able to read and correct the result before it affects a customer, business record, or business decision. We also plan what staff should do when the workflow fails, a service is down, or a result is unclear. The team needs to know how to pause the workflow or undo a connection. The right controls depend on your systems and data.

A demo shows how a workflow might work. A demo or test is not proof that we have set up this kind of system for a client. It also does not prove the workflow is ready for real work. Our Archery Sarasota example shows website work. It is not proof of a client AI system. We keep website examples, our own tools, and proposed AI work distinct.

What happens after the test?

If the tests support moving forward, we agree on who runs the system, who can use it, and how it will be checked and kept up to date. We also agree how to hand the work over. If the tests do not support it, we stop, and you keep your current process. We do not promise that AI can work on its own or deliver a specific business result.

Guides that shape our questions and tests

We use NIST guidance to ask who a task may affect, what could go wrong, and what to test. The guidance is voluntary and does not certify a project. Read NIST's AI Risk Management Framework overview and our guide to choosing a first AI workflow.

Before choosing an AI provider or feature, we check its current data rules. Those rules depend on the tool and its settings, so a provider's policy is not a privacy promise for a project. If a project uses OpenAI, read OpenAI's current API data rules.

Use the instrument. Trust the Source.

AI is a tool. God alone is the source, provider, and guide. You do not have to share our faith to work with us. People stay responsible for how the tool is used. Read where we stand on AI. For examples, clear rules, and human review, read Teaching AI Like a New Employee.