How to Scan Emerging AI Technologies with AI

What emerging-technology scanning should answer

A useful emerging-technology scan tells a technology-scouting or R&D team three things: is activity in a defined technical direction actually accelerating, under what exact query and time window, and which specific records are worth a closer read. It should also say clearly what it has not established, because by definition, an emerging signal is early and still noisy.

AI-agent tool-use and computer-use technology is a good test case. The idea that a large language model can call external tools, APIs, and eventually full computer interfaces to complete multi-step tasks has moved from research demos to shipped products inside a few years. Whether that shift is visible, and how fast, is exactly the kind of question a scan should answer with evidence rather than impression.

Why keyword-only scanning misses real signals or drowns in noise

A single keyword search either returns too little or too much. Searching only “AI agent” returns hundreds of thousands of unrelated hits, robotics agents, insurance agents, multi-agent simulation papers, because the vocabulary is reused across unrelated fields. Searching only a narrow phrase like “computer-use agent” misses the surrounding vocabulary (“tool calling,” “agentic workflow,” “function calling”) that the same technical idea is filed and published under.

Comparing years without keeping the query and date field unchanged is just as unreliable. Many patent applications publish about 18 months after the earliest filing, and indexing can add further delay, so the newest period is usually incomplete.

How the Skill works

The Skill turns a technical direction into a bounded signal brief in four steps, keeping the query and date basis fixed throughout.

What the connected literature adds

Two directly relevant papers surfaced through the same run: “BEARCUBS: A benchmark for computer-using web agents” (Song et al., ArXiv, posted July 2025), which reports that a leading computer-use agent reached 65.8% accuracy on 111 real-world browsing tasks versus 84.7% human accuracy; and “Build the web for agents, not agents for the web” (Lù et al., ArXiv, posted June 2025), a position paper proposing that web interfaces be redesigned for agents rather than retrofitted for them. Both are recent, both are directly on-topic, and both show that the research literature still treats computer-use agents as unsolved rather than mature.

For this partial run, several widely cited earlier papers on LLM tool-augmented reasoning and tool-calling benchmarks from 2023 were not retrievable through the connected literature search. That is a coverage gap in this specific search pass, not evidence that the underlying research does not exist, a broader literature search should close it before this brief is treated as complete.

Prepare, install, and run

Provide the technical direction in your own words, the adjacent vocabulary you already know, the date field to keep fixed (application date is usually more stable than publication date for recent activity), the jurisdictions or applicants you care about, and how the result will be used.