Deep Research Agents and How They Are Trained

A deep research (DR) agent is an agent specialized for open-ended information work: you ask a complex question, and it browses, retrieves, reasons across many sources, and writes a structured, cited report. OpenAI's Deep Research, Gemini Deep Research, Perplexity, and Grok DeepSearch are the famous examples. They are built on the exact harness ideas from this guide (loop, tools, planning, memory, multi-agent), so studying them sharpens those ideas, and they add a new dimension we have mostly skipped so far: how you train an agent to be good, not just prompt it. This chapter draws on two academic surveys: "Deep Research Agents: A Systematic Examination and Roadmap" and "Deep Research of Deep Research: from transformer to agent."

The formal definition from the survey: DR agents are "AI agents powered by LLMs, integrating dynamic reasoning, adaptive planning, and iterative tool use to acquire, aggregate, and analyse external information, culminating in comprehensive outputs for open-ended informational research tasks." Compared with plain RAG (which boosts factual accuracy but does not sustain reasoning) and plain tool use (which depends on fixed workflows), DR agents add autonomy, deep reasoning, dynamic planning, and real-time adaptation.