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

What this post added

This post argues that the current focus on AI model reliability (accuracy, bias, hallucination) is insufficient for production AI agents. It emphasizes the critical need for system-level reliability, specifically Durable Execution, to handle failures in long-running, multi-step workflows. The author contrasts the limitations of current AI agents in recovering from failures with the capabilities of Temporal's Durable Execution, which provides checkpointing and resumption for distributed processes. The post uses examples like the Google Antigravity incident and METR's research on time horizons to illustrate the problem and positions Temporal's infrastructure as a solution for AI agent reliability.