Agentspan Review: Production-Ready AI Agent Infrastructure

Most AI agents work great… until something breaks.
A dropped connection, failed API call, or crashed process can wipe out the entire execution state and force you to start over from scratch.

In this video, I break down why most AI agents fail in production and how Agentspan solves the durability problem for long-running AI workflows.

We’ll cover:
• Persistent execution state for AI agents
• Resuming workflows after crashes or disconnects
• Human-in-the-loop approvals and paused executions
• Observability, retries, and debugging
• Distributed worker architecture for scalable AI systems
• How Agentspan uses Netflix Conductor for durable workflows
• Why this approach works with LangGraph, OpenAI SDKs, and existing agent frameworks

If you’re building AI agents that need to survive failures, handle real-world workflows, and scale reliably, this is worth checking out.

Resources:

● GitHub: https://github.com/agentspan-ai/agentspan
● Docs / Quickstart: https://agentspan.ai/docs/quickstart
● Community Discord: https://discord.gg/ajcA66JcKq

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