Research agents are emerging as a crucial application of AI, capable of processing vast amounts of information and synthesizing insights instantly, thus overcoming human limitations such as memory and reading speed. The development of state-of-the-art research agents involves creating sophisticated software layers, known as agent harnesses, to manage context, orchestrate tasks, and handle errors in rapidly evolving AI models. The authors highlight the importance of designing these systems to adapt to future model improvements without relying on fixed assumptions, as well as the necessity of context engineering to maintain efficient, relevant data retrieval. By leveraging advanced search features and focusing on streamlined toolsets, they reduced token consumption and achieved state-of-the-art results while maintaining reliability and efficiency. Through a combination of simplified orchestration and careful monitoring, the authors advocate for a balance between autonomy and control in building production-grade agents, emphasizing that qualitative improvements in reliability and efficiency are more valuable than optimizing for numerical evaluation scores.