What the Deterministic Rules Engine Does
Practice this questionWANT TO UNDERSTAND THE CONCEPT FIRST?Learn What the Deterministic Rules Engine Does
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
A simplified example:
def next_step(state, facts):
if not state.customer_verified:
return "request_verification"
if facts.get("transaction_id") is None:
return "ask_for_transaction"
if not state.dispute_reason_confirmed:
return "ask_dispute_reason"
if not state.action_confirmed:
return "request_confirmation"
return "submit_approved_dispute"This is illustrative pseudocode, not a complete production dispute policy. Real implementations should also enforce authorization, idempotency, connector error handling, and audit requirements.
The key difference is that the next step comes from explicit rules and state, not from an open-ended LLM instruction.
Adapted from 19_deterministic_workflow_engine_learnings.md · “4. What the Deterministic Rules Engine Does”. Code examples are study fragments. Difficulty and core labels are editorial estimates; version-specific behavior should be checked against your project.