Your Agent Works in the Demo. Now Make It Survive Monday.

Building a production-ready AI agent requires moving beyond a simple demo loop to a practice called harness engineering. This involves creating a robust wrapper around the model that constrains its actions through permissions, sandboxes, and human oversight, rather than limiting its reasoning. Frameworks like DeerFlow and Claude Code illustrate this shift from rigid pipelines to model-driven steps within code-enforced boundaries. The article also cautions against relying on benchmark scores, advocating instead for a thorough review of execution traces to truly understand an agent's performance and limitations.