AI for Research Efficiency, episode 3: Agents at work: a literature review
Narration: an AI-generated voice (ElevenLabs).

[0:00] Agents at work
57 files on one research question: papers, spreadsheets, Word tables and slide decks.
More files than a browser chat takes at once. Here, a team of AI agents reads them all, and checks every number against its page.
AI for Research Efficiency. Episode 3: Agents at work.
Last time, you installed an agent and asked it a first question.
In this episode, a literature review by several agents at once. You write the rules, give each agent one job, and check what they find.

[0:40] The question
The question comes from Arkansas, which plants nearly half of the country's rice.
Rice fields are usually kept flooded, and flooded soil gives off methane.
Alternate wetting and drying lets the water drain away between floods.
How much methane and water does that save? What happens to the yield? And do the Arkansas results agree with the rest of the world?

[1:07] The folder
The folder holds copies of 57 files that are free to share, in 8 formats. The PDFs alone run to 500 pages.
A browser chat takes 10 to 40 files at a time, and it can't reach the folder on your computer. And this job needs every number traced back to its page.

[1:28] The rules
First, the rules. They go in a plain text file in the folder, which the agent reads before it starts.
Claude Code reads a file called CLAUDE.md. Codex and Antigravity read AGENTS.md.
Write it as you'd brief a research assistant: the question, what to extract, and how to cite it.
Every number with its page, sheet and cell, or slide. Quotes, word for word. Anything uncertain, marked. And never change the source files.

[2:04] The jobs
Then the jobs. Each kind of agent gets a short description of its own: a reader, a checker and a writer.
Readers pull out the evidence. Checkers trust nothing: they open every source again. The writer drafts the answer from checked rows only.

[2:24] The request
Then one request, in plain words.
The run is long, so it works in auto mode, the faster speed from episode two. That's why the folder holds only copies.

[2:40] Eight readers
The lead agent splits the files into 8 batches, by topic, and starts 8 readers at once.
Each reader works through its own batch. From a PDF, it takes the text with the table columns in place, or looks at the page as an image. For Word, Excel and PowerPoint, it writes a short Python script.
Every finding becomes a row: one number, for one outcome, with its file, its exact place, and the words it came from.
In under 6 minutes, the readers are done: 667 rows, from 40 of the files. The other 17 had nothing on the question, or only pictures, and the readers said which.

[3:26] Eight checkers
Then 8 checkers, again at once. Each one opens every cited page, cell or slide, and compares it with the row.
573 rows are confirmed, 94 corrected, and none rejected. Most corrections are small: the right page, or how a study is described.
One changes the meaning. A reader filed an EPA sentence, "as much as 50 percent", as a comparison: flooding fields in winter, or not. And it said the study was in California.
The checker read the page again. The sentence gives the share of a year's methane released during winter flooding, and the page never says where. The right number, but the wrong claim.

[4:16] The answer
Last, the writer drafts the answer from checked rows. The lead agent then checks that every statement carries its citation, and fixes the ones that don't.
Across 10 meta-analyses, drying cut methane by 31 to 62 percent: about half, on average.
It saved a quarter to two fifths of the water, depending on what's counted. Yield held on average, unless the drying was severe.
Nitrous oxide went up, so the climate gain is smaller than the methane cut.
And in Arkansas, field trials saved 34 to 50 percent of the irrigation water, with no loss of yield: in line with the world's studies of irrigation alone. But the folder has no Arkansas measurement of nitrous oxide under this practice.

[5:07] Check it yourself
Now it's your turn. Pick a sentence, follow its citation to the row, and the row to the page.
Here's the water figure: row E0159. Page 2, Table 1: −25.7%, in the Water use column.
Do that for every figure your conclusions rest on.

[5:31] What it couldn't do
The agents also listed what they couldn't do: numbers shown only in charts, and key papers missing from the folder.
This run took 17 and a half minutes. That's one example, on one folder.

[5:50] Try it
Pause now, and try the practice folder from the companion page. Start your agent in the mode that asks first, and paste the request. You should get a table where every row names its page.

[6:04] Review
So: write the rules, give each agent one job, check against the source, and follow the trail yourself.
The rules file, the job descriptions and the practice folder are all on the companion page.