EP 18

In this episode of The Build, Cameron Rohn and Tom Spencer dig into AI agent development and the tooling shaping modern startups. They begin by framing AI agents and memory systems as the core technical challenge, unpacking trade-offs between short-term context windows and long-term vectorized memory for stateful interactions. They emphasize practical developer tools such as Langsmith for orchestration, MCP tools for model control, and integrations with Supabase as a low-friction data backend. The conversation then shifts to developer workflows and deployment: how Vercel simplifies front-end delivery while Supabase and MCP tools handle realtime state and model telemetry. They cover technical architecture decisions, comparing monolithic LLM pipelines with modular agent architectures and highlighting costs, latency, and observability trade-offs. They explore building in public strategies and entrepreneurship insights, advising founders to iterate transparently, instrument experiments, and open-source components to grow community adoption. They weave in startup monetization tactics, developer experience priorities, and recommendations for selecting frameworks and approaches that reduce cognitive load for teams. Throughout, the hosts balance technical depth with actionable advice for builders. They end with a forward-looking takeaway: prioritize composable architectures, robust memory systems, and public iteration to accelerate product-market fit and scale AI products responsibly.