chapter eight
8 The Workflow Service: orchestrating and deploying AI applications
This chapter covers
- Building the runtime server that turns decorated functions into HTTP services
- Async job storage, polling endpoints, and progress reporting
- Deploying with containers for concurrency, automatic retry, and health endpoints
- Calling workflows sequentially and in parallel through a single SDK method
- The Workflow Service's gRPC contract and the deployment pipeline
- Runtime management with Kubernetes
Every platform service we have built so far solves one piece of the AI application puzzle. The Model Service generates responses. The Session Service remembers conversations. The Data Service retrieves organizational knowledge. The Tool Service calls external systems. The Guardrails Service enforces safety policies. The Observability Service tracks costs, latency, and quality. Each service has a gRPC contract, an SDK client, and a clear responsibility. But none of them can turn a developer's code into a running, scalable application that external clients can call.