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Lessons12
Another useful architectural distinction is between a workflow engine and the application that uses it.
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
Understand the architecture, its boundaries, and the practical engineering trade-offs.
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.
A workflow can be represented as a graph:
A useful concept I learned is Out-of-Scope (OOS) Intent Detection, sometimes called Out-of-Domain (OOD) Intent Handling.
The LLM acts as an information extraction layer, not as the authority for workflow execution.
A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.
| 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 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.
An important lesson is to test the deterministic engine without depending on live LLM variability.
Interview questions12
Another useful architectural distinction is between a workflow engine and the application that uses it.
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
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.
A useful concept I learned is Out-of-Scope (OOS) Intent Detection, sometimes called Out-of-Domain (OOD) Intent Handling.
A workflow can be represented as a graph:
A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.
| 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 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.
An important lesson is to test the deterministic engine without depending on live LLM variability.
The LLM acts as an information extraction layer, not as the authority for workflow execution.
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