Observability is not enough

The evolution of software development in the AI era has highlighted the limitations of traditional observability tools, which focus on monitoring and diagnosing issues but fail to proactively identify or address the root causes of problems in distributed, cloud-based systems. AI-driven code writing has accelerated development but also increased the frequency of production incidents, with 94% of teams acknowledging faster output and 91% expressing caution about deploying AI-written code. This has created a pressing need for runtime control, a model that extends observability by enabling automatic detection and remediation of issues in real-time, thereby reducing the time between problem identification and resolution. Runtime control is particularly crucial for managing the unpredictable nature of AI agents, which are inherently nondeterministic and can behave unpredictably even to their creators. By allowing for progressive release, real-time governance of AI behavior, and automatic rollback of problematic changes, runtime control offers a foundation for an AI software factory where the development lifecycle becomes more autonomous and efficient, allowing engineers to focus on strategic goals rather than firefighting issues.

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