Exploring concrete user safety patterns, from authentication and authorization to production-grade failure handling, with real-world architectures and actionable takeaways.
Most ‘AI agents’ demos hide a complex runtime underneath. Here is what actually runs in production — the planner, the tools, the memory layer, the guardrails, and the observability stack that holds it all together.
A working engineer’s guide to LLMOps and GenAIOps: the pipelines, evaluations, guardrails, and observability patterns that turn LLM prototypes into reliable production services.
AI gateways sit between applications and foundation model providers, unifying auth, routing, caching, and observability. This post breaks down the patterns, the production wins, and the open source options like LiteLLM and Portkey.
Agentic APIs trade the predictable request-response contract for an autonomous, tool-using endpoint that plans, retries, and adapts. This post covers the architecture, observability, and security patterns needed to ship them reliably.