The model call is rarely the hardest part of an AI workflow. The failure usually appears one step later: when a draft, classification, or extracted field has to cross a boundary into another system.
A useful way to think about the design is to treat every handoff as a contract.
1. Define the handoff before choosing the model
For each step, write down:
- the trigger
- the context the step is allowed to see
- the exact output schema
- the checks that must pass
- the system that owns the next action
- the person who handles exceptions
For example, an invoice workflow might accept an uploaded document, extract a small set of fields, validate the vendor and amount, route uncertain cases to a queue, and only then create a draft record in the accounting system.
The important detail is that the model is not the owner of the entire process. It performs a bounded interpretation step.
2. Keep deterministic checks outside the model
Use code or system rules for things that should not be persuasive:
- required fields
- allowed status transitions
- duplicate detection
- amount thresholds
- date formats
- permission checks
- whether an approval is required
This makes the workflow easier to test and easier to explain when something goes wrong.
3. Make the exception path visible
A workflow is not production-ready if it only describes the happy path. Define what happens when:
- the source document is incomplete
- two systems disagree
- confidence is low
- a customer asks for an exception
- the requested action is irreversible
In those cases, the correct output may simply be a review item with the source context attached. That is a useful result, not a failure.
4. Measure rework, not just model accuracy
A high extraction score can still produce a bad workflow if people spend time correcting records or checking every result manually.
I would track:
- percentage of items completed without correction
- exception rate
- average review time
- duplicate or rejected actions
- time from trigger to completed handoff
- percentage of work that still requires re-entry
These measures tell you whether the workflow is reducing operational friction.
5. Start with one narrow boundary
The first pilot should usually connect one trigger to one structured outcome. For example: receipt received → fields extracted → review queue → reimbursement draft.
I wrote a practical example of this kind of workflow, including where validation and human review belong: https://kelenai.com/insights/expense-report-automation/
The broader lesson is simple: the most valuable AI workflow is often not the one with the most impressive model. It is the one whose handoffs are explicit, reversible, and easy for a person to supervise.
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