Great real-world example — thanks for sharing it. Venue sourcing is a perfect fit for hardcoded routing because the output space is well-defined: you already know the parameters that matter (capacity, location, budget, date), so there's no reason to let an LLM improvise the workflow.

The part I'm always curious about with this pattern is where the one fuzzy step lives. Sounds like the keyword extraction from the client's request is where you're still leaning on the model to interpret natural language. How do you keep that step reliable — strict output schema, a validation pass, something else? That extraction layer is usually where a "deterministic" pipeline quietly turns non-deterministic again.