Building Deep Research: How we Achieved State of the Art

Back to Articles Building Deep Research: How we Achieved State of the Art Team Article Published November 24, 2025 Upvote 35 +29 Michael Griff michaelgriff Follow Tavily Dean Sacoransky deansaco Follow Tavily Noah Nefsky noahnefsky Follow Tavily Research agents are rapidly becoming one of the most important applications of AI. Research is a foundational knowledge-work task: collecting, reading, and synthesizing information underpins everything from writing and decision-making to coding itself. Yet human-driven research is constrained by memory, reading speed, and time. AI research agents, by contrast, can process vast amounts of information, synthesize insights instantly, and scale effortlessly. Because of this, research agents are emerging as a top use case for AI today and will soon become a core subcomponent of broader agentic workflows across content generation, coding, sales, and more. In this post, we share the technical and philosophical lessons we’ve learned building a state-of-the-art research agent, and where we believe the field is headed. Building for the Future Agent Harness The task of building an agent harness is to create a software layer that enhances a model’s runtime execution through context management, tool invocations, loop control, orchestration, and error handling. Building applications on top of rapidly improving models is, however, a modern engineering challenge. How can we design software today that absorbs the performance gains from future model...