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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

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.

The critical separation is between interpreting language and making business decisions.

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.

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.

A robust architecture uses multiple layers of protection:

A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.

Keep the LLM's responsibility narrow

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

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.

The critical separation is between interpreting language and making business decisions.

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.

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.

A robust architecture uses multiple layers of protection:

A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.

Keep the LLM's responsibility narrow

Deterministic AI Workflows learning module

Deterministic AI Workflows learning module

Deterministic AI Workflows learning module

Deterministic AI Workflows learning module