A single AI call handles a single task. Real work is rarely a single task.
A grant application needs to be read, classified, checked against eligibility criteria, summarised for a reviewer, and filed with the right records updated. That’s several steps, some of which need AI judgement, some of which are plain logic, and some of which need to hit another system. Orchestration is the layer that runs the whole thing reliably.
The engineering that matters here isn’t the prompting. It’s everything around it:
- Routing — sending each case down the right path, and escalating the ones that don’t fit any path.
- Tool access — letting the model read from and write to your actual systems, with tightly scoped permissions.
- Validation — checking the output is structurally valid and plausible before it’s allowed to act on anything.
- Human checkpoints — placed where the cost of being wrong is high, and nowhere else.
- Observability — logs of what ran, what it decided and why, so you can audit and debug it.
- Failure handling — retries, fallbacks, and a queue for the cases that need a person.
Done properly this is unglamorous software engineering with a model in the middle. That’s the point. The demos are easy; the thing that runs every day for two years without quietly corrupting your data is the hard part, and it’s what we build for.