How AI Engineers Improve Agentic Products With Evals

With headlines like "95% of generative AI pilots at companies are failing" making the rounds on social media, the natural question becomes: how do you land in the successful 5%? Having supported and developed multiple enterprise AI products, I've found that the answer is to build robust product evaluation frameworks. Doing this, however, is easier said than done. It requires: developing diverse example output sets, recruiting end-user SMEs to annotate data, clustering failure modes, and aligning LLMs with human preferences to scale observability. While this process is often overlooked and difficult, following it leads to successful improvement and continued development of agentic applications.