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Lessons12
Another useful architectural distinction is between a workflow engine and the application that uses it.
My biggest learning is that AI understanding and business control should be separate concerns.
One of the most important lessons I learned while working with AI-powered workflows is that an LLM does not need to make every decision.
A robust architecture uses multiple layers of protection:
An important lesson is to test the deterministic engine without depending on live LLM variability.
| Area | LLM-driven workflow | LLM-assisted deterministic workflow | |---|---|---| | Natural-language understanding | LLM | LLM | | Next-step selection | Primarily LLM | Explicit workflow rules | | Business decisions | Model-guided | Rule-d
Understand the architecture, its boundaries, and the practical engineering trade-offs.
The critical separation is between interpreting language and making business decisions.
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
The LLM acts as an information extraction layer, not as the authority for workflow execution.
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
A multi-turn workflow needs a reliable memory of what has happened. That is different from merely sending the whole chat history back to an LLM.
Interview questions12
Another useful architectural distinction is between a workflow engine and the application that uses it.
An important lesson is to test the deterministic engine without depending on live LLM variability.
| Area | LLM-driven workflow | LLM-assisted deterministic workflow | |---|---|---| | Natural-language understanding | LLM | LLM | | Next-step selection | Primarily LLM | Explicit workflow rules | | Business decisions | Model-guided | Rule-d
A robust architecture uses multiple layers of protection:
One of the most important lessons I learned while working with AI-powered workflows is that an LLM does not need to make every decision.
My biggest learning is that AI understanding and business control should be separate concerns.
Understand the architecture, its boundaries, and the practical engineering trade-offs.
The critical separation is between interpreting language and making business decisions.
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
The LLM acts as an information extraction layer, not as the authority for workflow execution.
Keep the LLM's responsibility narrow
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
Modules5
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module
Deterministic AI Workflows learning module