AI Agentic Research Revolutionizes Knowledge Work
Key Points
- AI agentic systems are rapidly transforming research across fields by automating tasks that would normally take humans hours or days, exemplified by Stanford’s multi‑agent “STORM” that produces fully‑cited Wikipedia pages in minutes.
- Human research begins with a question and proceeds through a structured workflow: defining the objective, planning the approach, gathering data, iterating on insights, and finally delivering an answer.
- Simple factual queries can be answered with a single search, whereas complex, multi‑step questions require reasoning, synthesis of diverse sources, and often speculative analysis—challenges that multi‑agent AI can tackle.
- Multi‑agent research platforms replicate the human workflow by assigning specialized agents to each step and equipping them with online/offline search tools and APIs, enabling them to define objectives, create plans, collect evidence, refine findings, and generate comprehensive answers.
Sections
- AI Agents Accelerating Research - The passage explains how multi‑agent AI systems such as Stanford’s STORM can automate the research workflow—turning simple prompts into fully annotated Wikipedia articles in minutes—by emulating the human, question‑driven process of inquiry.
- Agentic AI Research Workflow - The speaker explains a multi-step, multi‑agent AI process for addressing a complex, speculative legal question—whether Brown v. Board of Education applies to private schools on Mars—by defining objectives, planning, gathering data, refining insights, and generating answers.
- Human‑AI Collaborative Research - The passage emphasizes that future research leverages open‑source multi‑agent frameworks to augment—not replace—human scientists, enabling them to delegate routine tasks to AI and focus on high‑value innovation and decision‑making.
Full Transcript
# AI Agentic Research Revolutionizes Knowledge Work **Source:** [https://www.youtube.com/watch?v=TiNedLS_txU](https://www.youtube.com/watch?v=TiNedLS_txU) **Duration:** 00:07:35 ## Summary - AI agentic systems are rapidly transforming research across fields by automating tasks that would normally take humans hours or days, exemplified by Stanford’s multi‑agent “STORM” that produces fully‑cited Wikipedia pages in minutes. - Human research begins with a question and proceeds through a structured workflow: defining the objective, planning the approach, gathering data, iterating on insights, and finally delivering an answer. - Simple factual queries can be answered with a single search, whereas complex, multi‑step questions require reasoning, synthesis of diverse sources, and often speculative analysis—challenges that multi‑agent AI can tackle. - Multi‑agent research platforms replicate the human workflow by assigning specialized agents to each step and equipping them with online/offline search tools and APIs, enabling them to define objectives, create plans, collect evidence, refine findings, and generate comprehensive answers. ## Sections - [00:00:00](https://www.youtube.com/watch?v=TiNedLS_txU&t=0s) **AI Agents Accelerating Research** - The passage explains how multi‑agent AI systems such as Stanford’s STORM can automate the research workflow—turning simple prompts into fully annotated Wikipedia articles in minutes—by emulating the human, question‑driven process of inquiry. - [00:03:06](https://www.youtube.com/watch?v=TiNedLS_txU&t=186s) **Agentic AI Research Workflow** - The speaker explains a multi-step, multi‑agent AI process for addressing a complex, speculative legal question—whether Brown v. Board of Education applies to private schools on Mars—by defining objectives, planning, gathering data, refining insights, and generating answers. - [00:06:19](https://www.youtube.com/watch?v=TiNedLS_txU&t=379s) **Human‑AI Collaborative Research** - The passage emphasizes that future research leverages open‑source multi‑agent frameworks to augment—not replace—human scientists, enabling them to delegate routine tasks to AI and focus on high‑value innovation and decision‑making. ## Full Transcript
Agentic systems continue to make waves across industries,
and one of the most important use cases is a agentic research
or research done by agents.
And it makes sense whether you are a data scientist
or an engineer or a knowledge worker in finance
or medicine, or even academia,
research is often time consuming,
repetitive, and it involves sifting through mountains of data.
And yet AI agents can accomplish in minutes
what takes us humans hours or even days.
So, for instance, a team
at Stanford University in February 2024
published on a multi-agentic system
that called STORM
that takes research or does research
and then writes a Wikipedia page in minutes, complete with annotations.
Given a simple prompt, how can I agents accomplish this?
Well, first let's answer the more fundamental question
how do humans actually do research?
Research is one of those activities that's core to the human experience.
You can't get through middle school without writing at least one
research paper.
And yet, the word is kind of hard to define.
The word research actually comes from the Old French word
researcher.
Which means
to seek.
After
or to search for.
So that means that research
always starts with a question.
Someone seeking after the answer
to a question.
Oftentimes, many questions.
Sometimes the question is simple
and factual.
Say, for instance, you're a lawyer
and you want to understand, you know, what date was the case
Brown vs Board of Education decided?
Simple, factual.
You popped into, search engine of your choice.
Search
engine... and outcomes and answer.
May 17th, 1954.
This is why we have terms such as desktop research.
But more often the question is far more complex.
Say that same lawyer, once in office and preparing to defend a case
now has a new question such as...
Does Brown versus Board of Education apply to private schools on Mars?
There's no single search result for that.
The answer requires reasoning, understanding
of legal precedents, case law analysis, and even
speculation about legal frameworks that don't yet exist.
It requires a multi-step process.
In response to this complex question.
The first step in the process is,
of course,
define the objective.
The second step is making a plan.
Of course,
after making a plan or a research plan, we need to proceed in line
with that plan to gather the data.
And then we need to refine our insights
as we obtain them.
And lastly, we need to generate an answer.
This is where a agentic research comes in.
AI agents, specifically multi-agent systems
capable of reasoning and with access to numerous tools like custom
online and offline search tools, including search APIs,
can generate research findings by mimicking how humans
investigate, synthesize, and iterate on knowledge.
So, like the plan that a human would follow a
well designed agentic research system works in the very same way
it starts in response to a question
by defining
the research objective.
And this could be the job of one agent
in this multi-agentic system.
Next, it would break down that plan,
that original objective, into a series of steps.
Or what we humans
would call a research plan.
That could be the job of another agent in the system.
And then, of course, there would be an agent specific colleague
whose goal and tools
equip him to gather data.
Next, there would be an agent
or agents to run this out.
The last two steps, which is refining
based on the original steps and plan,
and the insights gather and then generating an answer.
Notice how similar
this is to the way that humans research.
It's iterative, contextual, and it builds on previous knowledge.
So if you're a data scientist, developer or researcher
interested in exploring your own use case in agentic research,
there are many examples out there that exist.
Many example implementations of multi-agent frameworks,
that provide you with numerous examples.
If you are a data scientist, developer, or researcher
are interested in exploring your own use case in agentic research,
popular open source multi-agentic frameworks exist.
But just remember, the future of research isn't just
humans or AI, it's humans plus AI.
The real power of agentic research is in augmentation, not replacement.
By offloading tedious research work to intelligence systems,
you get to spend more time on high value tasks like innovation,
experimentation, and decision making.