Why Not Let the LLM Control the Entire Workflow?
An LLM is useful for interpreting natural language, but its output is probabilistic. Two similar messages can produce different wording or interpretations. This is acceptable for many conversational tasks, but it becomes risky when the system must follow a fixed business process.
Consider a customer reporting an unfamiliar card transaction. The system may need to verify the customer, identify the transaction, classify the dispute, ask mandatory questions, and obtain confirmation before taking an action. These steps cannot be skipped just because an LLM generates a convincing response.
A deterministic engine helps enforce:
- Consistency: The same validated facts and state lead to the same rule-driven decision.
- Compliance: Mandatory checks and confirmations remain part of the workflow.
- Auditability: The system can explain which rule or transition produced a decision.
- Testability: Expected paths can be reproduced with scripted inputs.
- Safety: Sensitive actions require explicit authorization and confirmation.
Determinism applies to the rule evaluation and state transition given the same inputs, not necessarily to the raw LLM extraction step. Extraction must therefore be validated before it can influence the workflow.
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Why Not Let the LLM Control the Entire Workflow?
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An LLM is useful for interpreting natural language, but its output is probabilistic. Two similar messages can produce different wording or interpretations. This is acceptable for many conversational tasks, but it becomes risky when the syst
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