Deep Agents in Action

In this episode of The Build, Cameron Rohn and Tom Spencer dive into the practicalities of developing deep AI agents and the architectures that power them. They begin by showcasing a live demonstration on Cameron’s DGX Spark, illustrating the iterative process behind building intelligent agents capable of complex research tasks. This hands-on example highlights the integration of Langsmith for agent orchestration and the use of MCP tools to streamline development workflows. The conversation then shifts to the strategic benefits of building in public, where Cameron and Tom discuss how transparency in development fosters community engagement and accelerates feedback loops. They explore how leveraging platforms like Vercel and Supabase supports rapid prototyping and scalable deployment, enabling startups to iterate quickly while maintaining robust technical architecture. Next, they analyze key technical decisions, emphasizing modularity and extensibility in AI system design. Cameron underscores the importance of choosing flexible frameworks to adapt to evolving project requirements and optimize performance. The episode also touches on entrepreneurship insights, including monetization strategies for AI-driven products and the balance between open source contribution and sustainable business models. Looking forward, Cameron and Tom encourage developers and founders to embrace iterative development and public collaboration as essential pillars for innovation in AI. The key takeaway stresses that combining cutting-edge tools with transparent building practices accelerates both technical progress and entrepreneurial success.