How to Orchestrate Multi-Agent Workflows on Kubernetes with Agentspan

Agentspan is presented as an orchestration layer that turns AI agent definitions into durable server-side workflows, separating the lifecycle of deployment, worker serving, and execution to support scalable multi-agent systems. The walkthrough deploys Agentspan’s control plane, PostgreSQL database, credentials, and networking on Kubernetes through Helm, optionally using K3D for local testing, then connects a Python SDK to the port-forwarded service. It builds a company-research team in which a coordinator uses handoff to trigger a deep-analysis agent, which runs market and risk specialists in parallel through separate Python tools. The team is registered once with `runtime.deploy`, its tool workers remain active in a separate process through `runtime.serve`, and later requests invoke the registered workflow by name with `runtime.run`. Completed executions can be examined through the API and UI, where the coordinator and nested agents appear as distinct executions and timelines reveal handoffs and parallel work. The central operating model is that Kubernetes manages the control plane, agent definitions can fit CI/CD workflows, workers can scale independently, and execution history supports debugging and observability.

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