Four major companies recently released AI research agents, all nearly identically named. One stands apart.
In episode 24 of the Family History AI Show podcast, available today, Mark Thompson and I introduce a new class of AI models known as research agents. Since December, Google has released Gemini 1.5 Pro with Deep Reseach, China’s DeepSeek released R1, Perplexity released their Deep Research, xAI released Grok 3 with DeepSearch, and OpenAI released their Deep Research. While all of these models return multi-page, source-linked reports, the comprehensiveness of OpenAI’s Deep Research powered by their 01-pro model (or perhaps even a fine-tuned version of o3) deserves special attention; for now, it may be in a class by itself (it certainly stands alone in terms of cost: Plus subscribers ($20/month) are allowed 10 queries per month, while Pro subscribers ($200/month) get 120 queries per month).
What makes a “research agent” different? I think of them as having several ingredients:
- a strong LLM (for language and information processing)
- Internet access (for research and information gathering)
- Reasoning and agentic abilities (to plan, evaluate, and iterate)
The output of all these research agents from all the various vendors are multi-page, source-linked reports. But OpenAI’s Deep Research is producing reports of such comprehensiveness that experts in medicine, physics, law, chemistry, architecture, and many other fields are expressing astonishment. These reports are not perfect; every fact-claim must still be verified, as details can be mistaken between the research and writing stages, but across many fields, experts report significant time-savings even taking into account the follow-up fact-checking required of these tools.
Many others are starting to share early results from DeepSeek, Grok, Gemini, and Perplexity, but the expense of OpenAI’s Deep Reseach has made access more limited. Linked below are two reports generated by Open AI’s Deep Research, powered by their latest reasoning and internet-enabled models. The first is a guide to the census that new family historians might generate as they learn to explore the usefulness of that resource. The second is a biographic sketch of a lesser-known signer of the Declaration of Independence, a mentor of Madison and other founders, the only clergy to sign the Declaration, and an early president of Princeton; John Witherspoon is also my uncle (several times removed).
- Leveraging U.S. Census Records for Genealogical Research: A Comprehensive Guide (18 pages)
- John Witherspoon: A Comprehensive Research Dossier (30 pages)
These agentic research models benefit from prompting techniques designed to enhance and shape results. Mark and I will be taking a closer look at all these models in our next episode, and how researchers across disciplines are prompting them for the best effect. This quick dispatch is intended to suggest that folks should not dismiss research agents out-of-hand, especially if they are only reviewing the free products.
The release schedule of AI products is as intense now as we have ever seen. New “hybrid” or “adaptive” models such as Anthropic’s Claude 3.7 Sonnet, xAI’s Grok 3, and OpenAI’s GPT-4.5, all now released or expected imminently, may push these agentic research features off the front page of the AI news for a bit. But these comprehensive reports will continue to draw attention and scrutiny as we explore and discover their benefits and limits. Today.
