Get to know us, our leadership, development direction, and why we call ourselves applied AI experts.
With experience across industries,we deliver impactful projects in these key sectors.
Code is cheap. Human time and model context are not. That is the core shift behind Harness Engineering: instead of trying to write better prompts, teams getting more reliable value from coding agents increasingly design better systems around their agents – documentation, context loading, validation, and feedback loops that let models safely own more of the work.
Earlier this year, our team won an OpenAI Codex hackathon with HyperCode, a repository intelligence layer that prepares relevant context for coding agents before they start working. Building it gave us a concrete way to explore how the system around an agent shapes its work. For engineering leaders, the goal is more predictable delivery: less time spent reconstructing context and fixing avoidable agent mistakes, with consistent checks before changes reach review.
