REST APIs were designed for humans calling services through UIs. AI agents are not humans. What breaks when you expose your existing APIs to agents, and what the right architecture looks like.

Your LLM system works great in demos and degrades in production. The culprit is almost never the model. It's what you're feeding it. How to diagnose and fix context rot before it kills your product.

Multimodal LLMs integrate vision through two fundamentally different architectures. Knowing which one you need, and why, shapes every other technical choice in your build.

GenAI platforms don't fail because the models are bad. They fail because teams build everything at once. A practitioner's guide to layered architecture, from the minimal production-ready core to healthcare-grade guardrails.

Inference-time scaling lets you tune the latency-quality tradeoff at runtime instead of at training time. When to use Best-of-N sampling, beam search, iterative refinement, or one-shot generation, with real examples from clinical AI.

LLM-as-judge evaluators feel like quality assurance but behave like rubber stamps. They fail hardest on the outputs that matter most: edge cases, safety-critical errors, domain-specific nuance. What to do instead.