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.
Most tokens burned by an AI coding agent are spent on I/O and boilerplate, not reasoning. Here’s how to slash your bill by routing grunt work to a cheaper model while keeping your frontier model for the problems that actually need it.
Coding agents spend most of their budget on file and symbol discovery, not on actual reasoning. Here is how precomputed context graphs change that, and why it matters for every team shipping agentic dev tools.