Why we built Agentspan: the production agent problem nobody wants to talk about

Agentspan presents a durable execution layer for AI agents intended to address production failures common in process-based frameworks, where crashes can lose in-memory state and force completed tool calls to run again. It argues that agent behavior resembles a dynamic workflow because each next action depends on model reasoning, tool outputs, and changing conditions, making deterministic testing and recovery difficult. Built on Netflix’s open-source Conductor workflow engine, Agentspan compiles agent definitions into server-executed workflows in which tool calls become durable tasks with independent state management, retries, recovery, and detailed observability. The platform is designed to preserve existing agent code and support frameworks including LangGraph, the OpenAI Agents SDK, CrewAI, and Google ADK, while enabling resumable runs, human approvals, guardrails, and inspection of recorded execution graphs. Agentspan is described as open source under the MIT license and can be run locally with its SDK and server.

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