Code as Agent Harness reframes the relationship between application code and AI agents. Instead of writing prompts, engineers design harnesses — structured code layers that define tools, guardrails, and orchestration logic for autonomous systems.
Modern coding agents default to grep-and-read when they touch a new repo. That choice is expensive, slow, and produces brittle edits. A new wave of zero-dependency C++ tools is replacing that loop with a ranked, deterministic call graph — and the economics of AI-assisted coding shift with them.
A working engineer’s guide to building an LLM agent from zero: the planning loop, tool integration, memory, guardrails, and the production patterns that turn a demo into a service.
How to design, build, and ship Model Context Protocol servers that turn LLMs into reliable, tool-using agents — with patterns drawn from Claude Desktop, Cursor, and real production deployments.
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.