Module 9 Lesson 2: Event-Driven Agent Design
·Agentic AI

Module 9 Lesson 2: Event-Driven Agent Design

Reacting in real-time. How to design agents that trigger actions based on external signals rather than internal loops.

Event-Driven Design: The Reactive Agent

In traditional agent workflows, the "Brain" is always in charge. In an Event-Driven system (like StrandAgents), the Environment is in charge. The agent only wakes up when a specific "Signal" is received.

1. The Trigger -> Action Pattern

Instead of a while True loop, the architecture looks like a list of listeners:

  1. Event: User uploads a PDF.
    • Trigger: ON_FILE_UPLOAD
    • Action: Summarize Agent starts.
  2. Event: Summarize Agent finishes.
    • Trigger: ON_SUMMARY_COMPLETE
    • Action: Translation Agent starts.

2. Decoupling Agents

In a CrewAI or LangGraph setup, Agent A usually "knows" about Agent B. In Event-Driven design, agents are Decoupled.

  • Agent A just publishes a "Success" event to a queue (like RabbitMQ or Redis).
  • Any other agent who is "listening" for that event can pick it up.
  • Benefit: You can add new behaviors (like a "Logging Agent") without touching the code of Agent A.

3. Visualizing the Event Bus

graph TD
    Trigger[Webhooks / Slack / API] --> Bus[Event Bus / Queue]
    Bus --> A1[Agent: Classifier]
    A1 -->|Event: Classified| Bus
    Bus --> A2[Agent: Database Writer]
    Bus --> A3[Agent: Slack Notifier]

4. Why This is "Sovereign" Friendly

Because event-driven agents are modular, they are perfect for Local Deployment. You can run one specialized agent on your RTX 3060 and another on your Mac, and they can talk to each other via events without needing a central orchestration server.


5. Code Concept: The Listener

@on_event("user.message.received")
def handle_new_message(payload):
    # This function is the 'Agent'
    # It only runs when the specific event happens
    response = model.generate(payload.text)
    publish_event("agent.response.ready", {"text": response})

# This is much simpler than a multi-node Graph!

Key Takeaways

  • Decoupling agents makes the system more maintainable and scalable.
  • Triggers move the logic from "Loops" to "Signals."
  • This pattern is perfect for high-concurrency applications.
  • It allows for Heterogeneous hardware (distributing agents across different machines).

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