Orchestration

The foreman never plasters a wall personally

The part that manages the flow is usually a very plain program. It is not making clever judgment calls — it is just following rules written down in advance: "when this finishes, do that," "if this value is empty, go here instead."

AI only gets called in where judgment is actually needed — summarizing a long document, sorting what kind of request came in, drafting something. Everywhere else sits a tool that is exact and dependable: search, a calculation, storage. Splitting the work this way lets you hand AI the parts it is good at and keep it away from the parts it is not.

That is also why the work gets broken into steps instead of handed over all at once. Throw one long instruction at something as a single block and you cannot tell where it went wrong. Break it into steps and you can see exactly which box it stalled in, and call back only that one.

The schedule carries both order and conditions

A flow rarely runs in one straight line. Two steps that have nothing to do with each other run side by side to save time, and a fork gets drawn wherever the result splits the path. A simple request gets answered right away; a complicated one gets routed to a step that looks things up first.

Some flows loop through the same step more than once: draft, review, fix what was flagged, review again. When that happens, you have to cap how many times it can loop. Without a cap, the steps just keep handing corrections back and forth and the job never finishes.

One step's output is the next step's raw material

Most trouble happens right at the seam between two steps. If the step before was supposed to hand over a list and instead hands over one solid block of text, the next step cannot use what it received and stalls. Put an AI that answers in ordinary sentences in the middle of the chain and this mismatch happens often.

That is why a check gets built into the seam. It confirms the result matches the agreed shape, and if not, asks again or fixes it before passing it along. Trimming what gets passed down to only what is needed matters too. Push everything a step produced straight into the next one and time and cost snowball.

Writing down what to do when something goes wrong

A flow can break at any point. The tool it meant to call does not answer for a moment, it hits a usage limit, or the answer comes back looking nothing like expected. A well-built flow spells out, in advance, how many times to retry, what other path to take if that fails, and where to stop and call in a person.

Steps that cannot be undone need to be flagged separately: sending an email, making a payment, anywhere that cannot be walked back once it happens. In front of a step like that, you place a spot for a person to confirm, or you make sure the same request cannot be processed twice by accident.

Keeping a record of what has gotten how far is just as essential. Without a log, there is no way to trace which step things went sideways in when the result looks wrong.

A schedule set in advance versus one decided on the fly

Everything so far describes drawing the schedule ahead of time. With the foreman holding the order, results are easy to predict and it is clear where to look when something breaks. The tradeoff is that the flow simply stalls the moment it meets a situation nobody planned for.

The other approach lets the flow decide its own order as it goes, with the AI picking which tool to use and when, case by case. It handles unfamiliar situations better, but it can pick a different path for the same request each time, which makes results harder to predict and costs less steady. Real services usually strike a compromise: fix the main outline as a schedule, and grant freedom only inside the boxes that actually need judgment.