How to Build Reliable AI at Scale: Insights From Addy Osmani
•February 18, 2026
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Why It Matters
Enterprises and startups aiming to deploy AI at scale must prioritize reliability, governance and traceability over sheer agent count; mastering orchestration is essential to move AI from demos to systems that can safely serve millions. This shift will determine which organizations can operationalize advanced AI without creating unmanageable risk or maintenance burdens.
Summary
Addy Osmani, working to bridge Google DeepMind research with product and developer teams, urges builders to move beyond one-off demos toward production-ready AI systems. He frames development on a spectrum from “wild west” solo experiments to enterprise-grade setups with quality gates, long-term maintenance and traceability. Osmani highlights that recent tools enable true multi-agent orchestration—agents that spawn, communicate and self-coordinate—but warns the core challenge is coordination and integration, not generation. He recommends focusing on modest, well-orchestrated agent sets that solve real problems while preserving control and auditability.
Original Description
“There are people who are out there so leaning into the ‘YOLO, you just let the agents rip.’ But if you're really trying to build reliable, secure code that's going to touch perhaps billions of users, how much of that can you do?” asks Tim O’Reilly in this excerpt from his conversation with Google’s Addy Osmani. Addy’s job is to help developers and their organizations succeed with Google's AI tools. Here’s his quick take on balancing velocity with reliability, scale, and corporate policies, and why the real frontier for enterprise will be “orchestrating a modest set of agents that solve real problems while maintaining control and traceability.”
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