AI coding agents move the bottleneck in software teams. Once a model can produce a decent patch in minutes, the scarce work is steering: giving it the right context, catching drift early, preserving architecture, and making failure easy to catch and cheap to fix. This talk makes Harness Engineering concrete: the platform work around agentic coding. A useful harness turns the repository into an environment agents can operate inside safely. That means project commands, task-specific skills, architecture maps, coding rules, tests the agent can run and understand, feedback loops, and guardrails that keep changes observable and contained. You will leave with a practical checklist for building your own harness: making your codebase more agent-ready, reducing drift and cognitive debt, while keeping accountability where it belongs: with humans.