Deterministic Flow Pattern
The skill sequence is fixed in the job description before execution — the LLM processes output between steps and converses with the user, but doesn't decide what happens next.
Why This Pattern
If you already know the exact sequence a use case requires, letting the LLM "decide" the flow buys you nothing but unpredictability and cost. The build reasoning is to spend design-time judgment up front, in the job description, so runtime judgment stays limited to parameter collection and conversation — not sequencing.
The moment the next step depends on what a prior skill returns, you no longer have a fixed flow — that's Guided Skill Chaining. The moment the input shape itself varies by category, that's Dynamic Input Builder, layered inside an otherwise fixed flow.
When to Use It
- The use case genuinely has a fixed, well-understood sequence of steps.
- You want the LLM to process output and converse with the user at each step, without deciding what happens next.
- You need the reliability of a predictable flow more than the flexibility of a reasoning loop.
When NOT to Use It
- The use case is based purely on user prompt or intent, with no predictable step order.
- The application requires variable input schemas per request type.
How It Works in Workato
Keep the job description crisp. Avoid stuffing every step and tool into the prompt — it should read as guidelines, letting the genie execute the relevant skills in order. Overloaded, step-by-step prompts are a common cause of unreliable genies.
Source: Agentic Design Cookbook V2.0, p. 11