In this episode of The Build, Cameron Rohn and Tom Spencer dissect OpenAI’s Customer Service Agent Demo, spotlighting key advances in AI agent development and practical implementation strategies. They begin by analyzing the agent architecture showcased in the demo, detailing how the OpenAI Agents SDK orchestrates multiple specialized agents—such as the triage agent modeled after a supervisor—to streamline customer interactions. The conversation then shifts to technical frameworks, with Cameron highlighting the Triage Supervisor Model, Guardrail Checkpoint Question, and Train Ticket Test as essential patterns for robust memory systems and decision logic within agent workflows. They explore the integration of developer tools like Langsmith for observability, Vercel for scalable deployment, and Supabase as a backend solution, emphasizing how these platforms accelerate iteration and reduce technical overhead. Tom and Cameron discuss building in public, sharing insights on leveraging community feedback and open-source contributions to refine both the AI models and supporting infrastructure. They also touch on entrepreneurship, examining monetization strategies and the importance of transparent development cycles when launching MCP tools or similar startups. The episode concludes with a forward-looking perspective, encouraging developers and entrepreneurs to embrace modular architectures and public iteration. By integrating best-in-class tools and sharing progress openly, builders can navigate technical complexity while fostering innovation and trust within the AI ecosystem.