The original title is "Building Fault-Tolerant AI Agent Workflows with Temporal and CrewAI"

Short summary

This article presents a reference architecture for production-grade multi-agent LLM systems using Temporal as a durable orchestration layer and CrewAI as stateless reasoning units invoked inside Activities. It addresses three enterprise gaps that typical agent demos ignore: state survival across process crashes, explicit retry policies for LLM API failures, and unbounded human-in-the-loop approval waits without polling. The pattern of treating the orchestration engine as the source of truth — with versioned workflow patching for in-flight instances — is the core contribution.

• •Temporal provides durable state, replay-based recovery, and declarative retry policies that survive process death and deploys
• •CrewAI is deliberately scoped to stateless reasoning inside Activities, never imported into the deterministic workflow sandbox
• •Workflow patching via workflow.patched() enables safe code changes to long-running workflows without breaking in-flight instances

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