Agents aren't software. A conversation with Jeff Dean helped me see why.

As AI agents increasingly make decisions within live production systems rather than merely generating code before deployment, their probabilistic behavior challenges infrastructure assumptions built around deterministic execution. The author argues that agent systems should be treated as computation graphs containing models, tools, evaluators, branches, and side effects, requiring traditional goals of low latency, scale, cost control, and reliability to be reconsidered. Reliability may depend on costly safeguards such as redundant model outputs, parallel execution paths, voting, validation, and carefully defined commit boundaries. A central challenge is semantic recovery: after a long-running agent fails, systems must restore meaningful execution state, including validated constraints, decisions, intent, and side-effect boundaries, rather than simply restarting a process or restoring an indiscriminate snapshot. The proposed approach favors compact, explicit semantic scratch space and reentrant memory that distinguishes durable judgments from temporary traces, enabling agents to stop, inspect, fork, and resume coherently. Traditional virtual machines and containers isolate resources but do not capture execution meaning, leading the author to advocate for agent-native runtimes that unify state, side effects, and recovery; Runta is presented as an effort to make such long-running execution resumable, inspectable, and recoverable.

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