Most LLM agent demos die in production because teams skip the boring parts: tool contracts, eval harnesses, and observability. This post walks through the architecture, patterns, and evaluation loops that turn a clever prototype into a system you can actually operate.
Agentic AI is the shift from one-shot prompting to autonomous loops that plan, call tools, observe results, and recover from failure. This guide walks through the core loop, the production patterns, and the infrastructure that makes agents viable at scale.
Stanford’s agentic AI overview offers a clean taxonomy, but production engineers need to know how the four-agent model, tool-use loops, and memory architectures translate into deployed systems — and where they fall apart.
How NVIDIA’s TensorRT-LLM squeezes maximum throughput from a single GPU — and across a fleet — by fusing kernels, paged KV-cache, and continuously batching requests.
A deep dive into the loss detection, ACK handling, and congestion control loops inside QUIC, with a focus on how they behave under real production traffic on HTTP/3 and gRPC.