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.
New techniques let language models decide for themselves where to attend, cutting inference cost without retraining. We break down how gating, sparse kernels, and self-reflective decoding work in production systems.
AI agents aren’t chatbots. They execute code, hold credentials, and call APIs on their own. Here’s why that breaks traditional security models — and what teams are doing about it.
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.
We follow a single chat completion request from a client SDK down through the inference server, into the model runner, and finally into the CUDA kernels that execute on a GPU — with the bottlenecks and knobs along the way.