In this module you will
Describe each of the five patterns, draw it, and say what it costs in model calls and time.
Write each one as a small function over any "text in, text out" model callable.
Choose the right pattern for a task, and know the failure mode of each.
Test workflows without a real model by injecting a fake one.
Most useful LLM applications are not free-running agents. They are a fixed arrangement of model calls written by you, and the arrangement is what makes them dependable. Even real agents are built from these shapes inside their loop. If you know the five shapes you can solve most problems with code you can read, test and cost in advance.
Workflow
A system where your code decides the order of the model calls. Contrast with an agent, where the model decides what to do next (module 4 compares them). This split, and the five patterns below, follow Anthropic's engineering article "Building Effective Agents". Its advice is also this module's rule: start with the simplest pattern that works.
About the examples
sim.py is an imitation : a rule program that behaves according to the first line of the prompt (CLASSIFY, PLAN, WORK, ...). It exists so the patterns can be run and tested offline and so call counts are exact. Real models are less predictable, which is why each pattern below lists how it fails. The pattern code itself takes any function llm(prompt) -> str, so it works with a real model unchanged. No real model was called.
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Five shapes, and what each cost on the example (model calls) 1. Chain step gate step gate step 3 calls 2. Routing classify billing bug other 2 calls 3. Parallel input tone policy length merge 3 calls 4. Orchestrator plan worker worker worker combine 5 calls 5. Evaluator loop draft evaluate done feedback, max rounds 4 calls Counts are real output of the example code; they change with the input (orchestrator) and the number of rounds (loop). The five shapes and the number of model calls each used on the example. Real output of the example code.