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

AI agents are increasingly capable of performing complex tasks autonomously, but their reliability remains a significant challenge, especially during extended workflows. This issue stems not from the intelligence of AI models, which have continued to improve in accuracy and trustworthiness, but from the lack of robust infrastructure that can handle failures mid-process. An incident involving Google's Antigravity AI coding assistant highlighted this vulnerability when it mistakenly erased a user's entire D: drive, illustrating the urgent need for systems that can recover from partial failures without starting over. The AI industry has traditionally focused on enhancing model accuracy, but as AI agents transition from suggestion-based systems to action-oriented systems, the need for durable execution infrastructure becomes crucial. This infrastructure should function as a digital bookmark to allow seamless resumption of tasks, addressing the compound failure problem that arises when AI systems manage long-running operations autonomously. The integration of such infrastructure, akin to what companies like Temporal provide, is essential to ensure AI agents can reliably execute tasks over extended periods, preventing irreversible actions and maintaining continuity.