Why AI development creates a reliability blind spot for humans, and what to do about it

AI coding tools are rapidly increasing software output, but the expected productivity gains are being offset by higher change-failure rates, more production incidents, and greater time spent responding to outages. Traditional observability and SRE practices help teams detect, diagnose, and remediate failures after they occur, yet remain limited in anticipating unobserved risks before deployment. Chaos engineering offers a more proactive model by injecting failures to test resilience, and Gremlin has applied its accumulated fault data to create Foresight AI, an agentic reliability platform designed to identify potential failure conditions in preproduction, recommend or automate safe fixes, and validate major changes. The platform coordinates specialized agents across data collection, analysis, testing, operations, and program management while allowing humans to oversee strategic risk decisions through a conversational interface. The analysis argues that such agentic resilience systems could reduce the reliability blind spot created by AI-assisted development, though lasting success still requires organizational commitment, shared accountability, and a broader cultural focus on managing risk.

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