AI Engineering: Refining for User Outcomes

In this episode of The Build, Cameron Rohn and Tom Spencer dive into AI engineering with an emphasis on refining systems for clear user outcomes and sustainable product velocity. They begin by unpacking AI agent development and tools, contrasting agent orchestration patterns with direct API integration and citing Langsmith as a telemetry and orchestration example for tracing prompt/chain behavior. The conversation then shifts to developer tooling and workflows, where Vercel and Supabase surface as practical deployment and data-layer choices that accelerate iteration and low-latency experiments. They explore technical architecture decisions, debating microservice versus monolith trade-offs, the role of MCP tools in observability and model control, and how schema design and caching affect agent responsiveness. Next, they address building in public strategies, sharing granular tactics for transparent roadmaps, community contribution, and monetization signals that align engineering priorities with user value. Throughout, entrepreneurial insights thread practical advice on prioritizing measurable customer outcomes, choosing frameworks and approaches that reduce feedback loop time, and leveraging open source to scale adoption. They close with a forward-looking takeaway urging builders to iterate visibly, instrument rigorously, and align architecture and go-to-market decisions so future work directly moves users forward.