How do agents change runtime control?

Runtime control, traditionally designed for human-paced software releases, is challenged by the rapid operational pace of AI agents, which frequently bypass the assumptions that humans are present for decision-making, actions are isolated, and actors are users. By the end of 2026, it's anticipated that 40% of enterprise applications will incorporate AI agents, significantly up from 2025. These agents necessitate a shift in governance from individual API calls to entire action sequences, as high-stakes environments demand deterministic enforcement rather than relying on probabilistic system prompts. Feature flags become integral, providing a mechanism for runtime primitives and ensuring that agent actions are observable, reversible, and meet the standards of human code. This governance, which must operate locally within milliseconds, is crucial for enabling safe AI agent adoption, as it allows teams to deploy agentic features more rapidly and securely. The management plane handles runtime control configurations, while the data plane executes these controls, ensuring every agent action either passes a runtime check or pauses for human intervention. This model of governance not only supports faster shipping but also preempts issues through effective control at the sequence level, rather than relying on ad hoc fixes.

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