# Building Deep Research: How we Achieved State of the Art

DevFeed: [Building Deep Research: How we Achieved State of the Art](<https://devfeed.tech/articles/building-deep-research-how-we-achieved-state-of-the-art-7058.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Tavily/tavily-deep-research>)

Author: Michael Griff; Dean Sacoransky; Noah Nefsky

Published: 2025-11-24T17:40:14Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [AI-generated research reports](<https://devfeed.tech/topics/ai-generated-research-reports.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [building](<https://devfeed.tech/tags/building.md>), [compression](<https://devfeed.tech/tags/compression.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [hallucinations](<https://devfeed.tech/tags/hallucinations.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llms](<https://devfeed.tech/tags/llms.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [research](<https://devfeed.tech/tags/research.md>), [search](<https://devfeed.tech/tags/search.md>), [tools](<https://devfeed.tech/tags/tools.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

## AI overview

The article outlines principles for building deep-research agent harnesses that remain effective as models improve. It emphasizes simplified orchestration, reliable tool calling, and context engineering to reduce context overload, hallucinations, and latency.

## Source excerpt

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?