AI reliability is a decade-old problem. And we’re still only solving half of it

This post argues that AI reliability is a decade-old problem, with the industry focusing on model accuracy while neglecting system resilience for AI agents. It highlights the shift from AI systems that suggest to those that act, leading to critical failures when autonomous, multi-step workflows encounter issues. The author emphasizes the need for infrastructure that provides a 'digital bookmark' for workflows, enabling checkpointing, recovery, and resumption. This is framed as the core principle behind Temporal's Durable Execution, which is presented as the missing foundation for production AI agents to handle failures gracefully, preventing catastrophic outcomes like data loss or unauthorized actions. The post contrasts the historical focus on model reliability (accuracy, bias, hallucination) with the current need for system reliability in AI agents executing long-running operations.