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Two agents connected by an edge. The first classifies the user’s intent; the second reads that classification from the workflow context and composes a reply. This classify → respond pattern appears in nearly every customer-facing agent workflow.

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What it demonstrates

  • Connecting two nodes with an edge
  • Writing intermediate results to working.* context paths
  • Mixing providers: OpenAI classifies, Anthropic replies
  • Workflow-level guardrails

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Run it

sirenspec run docs/cookbook/sequential-pipeline/workflow.yaml --input "My order hasn't arrived yet."

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Workflow

docs/cookbook/sequential-pipeline/workflow.yaml
version: "0.1"

agents:
  classifier:
    model: "openai:gpt-4o-mini"
    system: |
      Classify the user's message into one of these intents: question, complaint, feedback, other.
      Respond with ONLY the intent label, nothing else.

  replier:
    model: "anthropic:claude-haiku-4-5-20251001"
    system: |
      You are a customer support agent. The user's intent has been classified.
      Compose a short, helpful reply appropriate for that intent.

nodes:
  classify:
    agent: classifier
    writes: working.intent

  reply:
    agent: replier
    writes: output.reply

edges:
  - from: classify
    to: reply

guardrails:
  - injection
  - length
This workflow mixes providers: OpenAI handles classification, Anthropic writes the reply. Each agent independently resolves its own provider at runtime.

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How data flows

  1. classify receives the user’s message and writes a single intent label (e.g. "complaint") to working.intent.
  2. reply receives the output of classify as its user message and composes the final response.

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Graph

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Next steps

Conditional Pipeline

Route to different handlers based on the classification.

Telephone Game

Chain five agents and watch meaning drift.