Building Deep Research: How we Achieved State of the Art

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.

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 releases?

This requires forecasting how models will evolve, staying optimistic about their progress, limiting assumptions, and avoiding hand-crafted optimizations.

We learned this the hard way seven months ago, when we had to abandon our first attempt at deep research and rebuild the entire system from scratch. The first architecture was complicated and sophisticated (we thought this was a good thing), but its assumptions became bottlenecks when the next generation of models arrived.

This approach differs from traditional context structuring in a ReAct agent-based architecture. Typically, tool calls and outputs are propagated through the tool calling loop, with previously retrieved/generated tokens being persisted in the context window on each subsequent iteration. This pattern can be seen in LangChain’s Open Deep Research agent implementation, and from a token consumption perspective, it can be modeled by the following quadratic series, where is the amount of tokens the tool calling model is invoked with on each tool calling iteration, and is the number of tool calling iterations.

Similarly, tools should evolve to support LLMs and widely adopted agent harnesses. The best tools should perform some context engineering on the tool side, abstracted away from the agent. They should return only the most relevant data instead of dumping large volumes of tokens into the context window. As a tool provider, we’ve invested heavily in our advanced search feature, which has context engineering baked in. This in turn lowers hallucinations and latency for the downstream agent processes.

Over the last seven months, model capabilities have quietly but meaningfully evolved (especially in their tool-calling abilities). This single optimization focus has pushed us from workflows to agents. We believe future models will be trained to solve the current pain points of agent developers. Every model is ultimately consumed by a harness, so models should evolve in service of that harness. We hope to see models improve in high-recall summarization (for context compression), tool-calling reliability, and concision in writing.

Long-horizon research tasks expose a fundamental challenge in current agent design: the task of maintaining a clean, optimized context window over time. If curating context is not a task the engineer pays close attention to, the agent is almost destined for failure. The following outlines our thinking around this concept within the deep research domain.

Using Tavily’s Advanced Search is the natural first step to take in overcoming this challenge, in that it abstracts away the processing of raw web content and returns only the most relevant content chunks from each source. In leveraging this functionality, we let Tavily Search do the heavy lifting and allow Tavily Research to reap the benefit, gathering the most valuable content in a latency-efficient manner.