Agent runtime

Workflow Runtime

The engine that runs an AI agent workflow as a durable, observable, restartable process instead of a one-shot script — what separates an agent demo from an agent deployment.

Operating principle

Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.

What a runtime provides

Durable execution (state survives restarts), tool invocation through governed surfaces, retry policy per step, timeout enforcement, idempotency for external side effects, deterministic replay for debugging, and traces that link parent and child steps across model, tool, and retrieval boundaries.

  • Durable state across restarts and long-running steps
  • Idempotent tool invocation with retries and timeouts
  • Deterministic replay from traces
  • Versioned graphs with promotion paths

Substrates we build on

LangGraph (LangChain) and the OpenAI Agents SDK for the local execution shape; Mastra for TypeScript-native workflows; Inngest for event-driven step functions; Temporal for tasks that span hours or days. We do not write a competing framework — we write the contract around the framework.

When the runtime matters

When the workflow takes longer than a single HTTP request, when tools have real side effects, when humans approve, when costs must be attributed per tenant, or when you need to replay a six-step trace to debug last Thursday's bad output. A pure chat session needs none of this; a production workflow needs all of it.

Related resources