Deep research mode
Deep research mode is the multi-hour LLM-agent feature that runs an autonomous research loop — searches dozens of sources, reads, synthesizes, cites — and returns a structured report. OpenAI Deep Research, Perplexity Pro Research, Gemini Deep Research, Claude Research are 2026 examples.
Standard chat answers in seconds; deep research takes 5-30 minutes and returns a multi-page report with citations. Behind the scenes: agent plans search queries, fans out across sources, reads pages, ranks evidence, synthesizes, drafts the report, self-critiques, iterates. The output is closer to a junior analyst's first draft than to a search result. Production gotchas: cost (each run can be $5-20 in API tokens), latency (users must wait), citation accuracy (not every cited source backs the claim), source quality (SEO spam can sneak in), reproducibility (same query gives different reports). Best use: starting point for serious research, not the final answer. By 2026 deep research is the headline feature of every consumer AI product tier.
When to use deep research mode
- First-draft research before human verification.
- Competitive landscape scans, due diligence prep.
Common mistakes
- Treating the report as final — every citation needs to be re-verified.
- Running deep research on simple fact-find queries — wasted minutes + dollars.
FAQ
What is deep research mode?
Deep research mode is the multi-hour LLM-agent feature that runs an autonomous research loop — searches dozens of sources, reads, synthesizes, cites — and returns a structured report. OpenAI Deep Research, Perplexity Pro Research, Gemini Deep Research, Claude Research are 2026 examples.
When should I use deep research mode?
First-draft research before human verification. Competitive landscape scans, due diligence prep.
What are the most common mistakes with deep research mode?
Treating the report as final — every citation needs to be re-verified. Running deep research on simple fact-find queries — wasted minutes + dollars.
Related terms
- Search-grounded generation — Search-grounded generation is the LLM workflow where every output sentence is anchored to a retrieved source — the production pattern behind Perplexity, ChatGPT Search, Gemini AI Overview, You.com.
- Research agent — A research agent is an LLM-driven agent specialized for multi-source synthesis — searches, reads, summarizes, compares, cites — packaged either as a product feature ([[deep-research-mode]]) or as a custom agent in n8n / Mastra / LangGraph / Pipedream.
- Background agent — A background agent is an LLM-driven worker that runs asynchronously — receives a task, executes for minutes/hours without a user attached, posts results when done. Cursor's Background Agents, Claude Code's async tasks, Devin are 2026 examples.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/deep-research-mode.md.