AI agents that run a multi-step research process on their own, searching, reading, cross-checking, and summarizing, instead of answering from a single prompt.
Also known as Deep research agent, autonomous research agent
Autoresearch describes an AI system given a research question that plans its own steps to answer it: deciding what to search for, reading multiple sources, checking them against each other, and assembling a written summary, largely without a person directing each step. It is the difference between asking a chatbot a question and getting an answer from its training data, versus assigning it an open-ended brief and getting back something closer to what a junior analyst would produce after an afternoon of digging.
The major AI labs each shipped a version of this in 2025 and 2026, usually branded as some flavor of “deep research.” What makes it autoresearch rather than a souped-up search engine is the looping: the system reads a result, decides that result raises a new question, goes and searches for that too, and keeps going until it judges the brief is answered.
It compresses the unglamorous part of strategy work. Competitive audits, category scans, and “what has this brand said publicly for the last two years” research used to eat the first day of any new business pitch. Autoresearch tools can produce a rough first pass in minutes.
It is a starting point, not a finished deliverable. Autoresearch tools cite sources, but they also miss context a strategist would catch, a competitor’s campaign that flopped for reasons the AI cannot infer from a press release, for instance. The output needs a human pass before it goes in front of a client.
It changes what junior staff spend their time on. Work that used to be an entry-level analyst’s first few weeks on an account, gathering background, is increasingly a prompt and a review, which shifts what agencies need junior hires to actually be good at.
A strategist pulling together a new business pitch for a beverage brand gives an autoresearch tool a brief: this brand’s last three years of campaigns, how competitors positioned against them, and any controversy worth knowing before the pitch. Twenty minutes later she has a document with sourced summaries of a dozen campaigns, a rough competitive map, and a flagged note about a product recall the brand’s PR team clearly worked to keep quiet. She spends the next two hours not gathering that information but arguing with it: checking the recall story against a second source, cutting a competitor comparison that reads as generic, and rewriting the summary in the agency’s own analytical voice before it goes in the deck.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.