AI reliability is a decade-old problem. And we’re still only solving half of it
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AI agents can reason through complex tasks but fail catastrophically when something goes wrong mid-workflow. Even at 85% per-step reliability, a 10-step workflow succeeds only ~20% of the time due to compounding failures. The author argues the industry focuses heavily on model-layer reliability (hallucinations, bias, accuracy) while largely ignoring infrastructure-layer reliability — checkpointing, crash recovery, and durable state across long-running workflows. A 2025 incident where Google's Antigravity AI wiped a user's entire drive illustrates the stakes: the agent could diagnose the failure but had no mechanism to recover. The solution proposed is Durable Execution — infrastructure that checkpoints workflow state so agents can resume after failures rather than restart from scratch. Temporal, the author's company, builds this infrastructure and positions it as the missing foundation for production AI agents.
