AI plus deterministic rules: where each belongs in a workflow
A practical way to decide which decisions an AI step should make, and which should stay in plain rules.
By the Zynapte team
Separate interpretation from decision
AI is good at interpreting messy input: reading a message, a document or a record and producing a structured result. It is a poor place to keep business policy, because its behaviour is probabilistic and harder to audit.
A useful split: let the AI step interpret (classify, extract, enrich), and let a rule decide what to do with the result.
Use confidence as an input to rules
When an AI step returns a confidence value, treat it like any other field. A rule can send high-confidence results down an automatic path and route the rest to a person.
- Define the output schema before you write the prompt
- Choose a threshold from reviewed examples, not intuition
- Log every AI input and output with the run
Keep people where the cost of error is high
Approvals are not a fallback for weak AI. They are a design choice for actions that are expensive to undo: payments, customer-facing messages, access changes.