Deterministic AI Workflows
Design reliable AI-assisted workflows with explicit rules, validated state, safe actions, and reproducible tests.
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Foundations & responsibility boundaries
State, rules & graph execution
Scope, safety & external actions
Testing, reuse & architecture choices
Production lessons & implementation
Overview
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.
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 syst
The Architecture at a Glance
The critical separation is between interpreting language and making business decisions.
What the LLM Does
The LLM acts as an information extraction layer, not as the authority for workflow execution.
What the Deterministic Rules Engine Does
Once facts are validated, the rules engine evaluates the current workflow state and chooses the permitted next step.
Workflow State and the Ledger
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.
Graphs, Nodes, and Transitions
A workflow can be represented as a graph:
Out-of-Scope Intent Handling
A useful concept I learned is Out-of-Scope (OOS) Intent Detection, sometimes called Out-of-Domain (OOD) Intent Handling.
Guardrails and Authorization
A robust architecture uses multiple layers of protection:
Connectors and External Actions
A workflow often needs to retrieve or update information through APIs, databases, or external services. These integrations are commonly called connectors or tools.
Why Scripted Testing Matters
An important lesson is to test the deterministic engine without depending on live LLM variability.
Reusable Engine vs. Application
Another useful architectural distinction is between a workflow engine and the application that uses it.
LLM-Driven vs. Deterministic Workflow
| 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
Challenges and Practical Lessons
Keep the LLM's responsibility narrow
A Practical Implementation Checklist
Understand the architecture, its boundaries, and the practical engineering trade-offs.
Key Takeaways
My biggest learning is that AI understanding and business control should be separate concerns.
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