AI moves faster than traditional software. New foundation models, APIs, agent frameworks, and evaluation techniques are constantly reshaping what's possible, making it difficult to build applications that remain reliable as the ecosystem evolves. This panel explores how engineering teams are designing adaptable AI architectures, reducing technical debt, and making technology choices that allow their systems to evolve without constant rewrites. Key takeaways → Designing AI systems that remain adaptable as models and tooling evolve. → When to build abstractions versus integrating directly with model providers. → Reducing technical debt while maintaining development velocity. → Future-proofing AI applications without overengineering.