In this episode of The Build, Cameron Rohn and Tom Spencer dive into the rapidly evolving landscape of AI agent development and the tooling that accelerates it. They begin by examining the technical architecture behind AI memory systems, highlighting how frameworks like Langsmith enable more sophisticated agent workflows through persistent context management. The conversation then shifts to developer tools and infrastructure, where they analyze the role of platforms such as Vercel and Supabase in streamlining deployment and backend services, emphasizing their impact on product velocity and iteration cycles. They explore building in public strategies next, discussing how transparent development and community engagement not only foster trust but also drive rapid feedback loops essential for refining AI products. The hosts delve into MCP tools, underscoring their utility in orchestrating modular AI components and enhancing developer productivity. Throughout, Cameron and Tom share entrepreneurial insights on monetization approaches and startup dynamics, stressing the importance of balancing technical innovation with market needs. Concluding with a forward-looking perspective, they underscore that success in AI development hinges on agile architecture choices paired with open collaboration. For developers and entrepreneurs alike, the key takeaway is that embracing transparency and leveraging robust, scalable tools are critical to building impactful AI-driven startups in an industry poised for exponential growth.