The Missing Infrastructure for AI Agents

Everyone can build an AI agent. Almost nobody knows how to run thousands of them safely in production. Over the past two years, we've watched AI agents evolve from demos into software that writes code, answers customers, automates workflows, and makes decisions. The challenge has shifted from building smarter agents to operating them reliably at scale. Having built one of Meta's first internal AI agents and now leading Guild.ai, CEO and Co-Founder, James Everingham has seen firsthand where production systems fail. It's rarely the model. It's the surrounding infrastructure: context management, permissions, observability, cost controls, versioning, evaluation, and governance. This session explores what changes when AI agents become real production systems instead of prototypes. He’ll cover the architectural patterns that separate successful deployments from expensive experiments, why "agent engineering" is becoming its own discipline, and the infrastructure every engineering team should think about before deploying autonomous software. Attendees will leave with practical frameworks for designing AI agent systems that are observable, secure, reusable, and capable of evolving over time, not just impressive during a demo. Whether you're building your first agent or managing hundreds across an organization, this talk offers lessons from operating AI where reliability matters.