Scaling AI Workflows with Event-Driven Microservices

Synchronous HTTP chains to large language models create massive production bottlenecks. A single slow prompt or unexpected upstream timeout cascades into a degraded user experience.

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The Event-Driven Pattern

When architecting CareerLens, we replaced blocking API calls with an asynchronous event broker:

1. Ingestion: User submits input; API immediately acknowledges with a task receipt.
2. Event Dispatch: An event document.uploaded is emitted to the broker.
3. Parallel Workers: Independent microservice workers extract features, query embedding vectors, and run domain LLM reasoning concurrently.
4. WebSocket Push: The frontend receives live incremental updates as each agent completes its milestone.

This architecture reduced multi-agent execution latency from over 30 seconds down to under 5 seconds while ensuring 99.9% fault isolation.