Agentic research

What it is

Agentic research describes AI tools that break a question into sub-questions, retrieve and read sources across several rounds, and produce a longer structured output with references. The system loops through plan, search, read and refine steps rather than answering from one retrieval pass. Outputs are typically multi-page briefings rather than short answers.

Why it matters

These tools read far more pages per question than a person browsing manually, so the pool of sources that can influence an answer is wider and less dependent on ranking in the top few results. For marketers, it changes who gets cited: clear, well-structured, evidence-backed pages can surface deep in a research run even when they do not rank first. It also means buyers can arrive at a shortlist having never visited a vendor site directly.

How it works

The agent decomposes the prompt, issues successive queries, fetches pages, extracts relevant passages into working context, and iterates until it judges the brief covered, then drafts an answer with links. Practitioners test their own category prompts in these tools, log which domains and pages get cited, and fix the gaps by publishing comparison detail, pricing clarity, methodology notes and dated data that an agent can lift cleanly. Server logs and analytics are checked for agent user agents to see what is being fetched.

When it applies

It applies to considered, research-heavy topics where buyers compare options, check evidence or build a shortlist, such as B2B software, professional services, regulation and technical how-to questions.

Examples

• A procurement lead asks a deep research tool for a comparison of mid-market payroll platforms for UK employers and receives a report citing twelve vendor and review sites.
• An agency runs the same ten category prompts in a deep research tool each month and records which of its client's pages appear in the citation list.
• A SaaS team notices research agent user agents fetching its pricing and integrations pages, and rewrites both so the facts are readable without expanding tabs.

How it is measured

• Citation rate: share of test research prompts where your domain appears in the source list
• Cited page mix: which URLs get referenced, and whether they are the pages you intended
• Agent crawl volume: requests from known research agent user agents in server logs, by URL
• Assisted outcomes: sessions and enquiries from users who mention an AI research report as their starting point