Predefined workflows or agents that plan?

Frontier models are making breakthroughs in science and mathematics, and intelligence has never been more abundant. Yet teams still argue over how much freedom to give their AI agents. One side writes every step in advance, as a predefined workflow. The other lets the agent plan its own steps. Both sides get something right.

Predefined workflows are predictable

A predefined workflow runs like an assembly line. Engineers lay out the steps in advance, and the model carries out each one as written. Every run takes the same path, so every output can be checked, and that is where the guarantees come from.

An accounts-receivable workflow matches each day’s customer payments to the invoices they settle, then applies them in the accounting system. Every run matches before it applies, so no payment is applied unmatched. And no step sends customers’ bank details anywhere but the accounting system.

The cost, of course, is the design effort up front, and brittleness when inputs vary in ways you didn’t anticipate. A customer pays three invoices with one wire, short by a disputed fee, and sends the remittance as a PDF no step expects. The payment drops into an exceptions queue for someone to work by hand.

Agents adapt, at the cost of predictability

An agent that plans its own steps takes cases like that in stride. Given the same wire, it reads the remittance, matches the invoices and logs the disputed fee, without anyone writing a step first. Each new model handles more of these cases.

But when the agent plans its own steps, the guarantees don’t come along. What the workflow enforced often becomes one more instruction in the agent’s AGENTS.md, weighed against everything else it reads. And the agent can be confidently wrong. It might decide a payment is close enough and skip the matching check. A script it wrote might upload a remittance, with the customer’s bank details, to an online PDF reader.

Both sides get something right

It can seem that teams have to pick one: the flexibility of an agent that plans, or the predictability of a predefined workflow. But it’s when their powers are combined that we get to build something outstanding. We think each side is right about a different thing. The workflow is right that the business rules must hold on every run. The agent is right that the steps don’t need to be written in advance.

So how do you make business rules hold firm when an agent plans its own steps? That’s where the next part of this series picks up.