Deep Research
Deep Research is an agentic AI capability in which a model autonomously plans and executes a multi-step research process—issuing search queries, reading web pages, and synthesizing sources—to produce a comprehensive, cited report without continuous human guidance.
Deep Research refers both to a category of agentic AI behavior and to specific product features in which a language model operates as a semi-autonomous research assistant. The agent decomposes a high-level research goal into sub-questions, executes iterative web searches and document reads, evaluates source relevance and credibility, follows promising links, and updates an internal knowledge state across many steps before producing a structured, cited report. The defining characteristic is that the agent determines its own research trajectory rather than responding to a single-turn query.
The workflow typically begins with a planning phase in which a reasoning-capable model outlines the information needed to address the query. It then enters a loop of searching, reading page content, extracting relevant facts, and deciding whether to pursue new leads or consolidate findings. Sessions commonly involve dozens to over a hundred web requests and run for several minutes to half an hour on complex topics. The final output is a long-form report with inline citations, often structured with headings, tables, and summaries.
OpenAI released a product feature named Deep Research in February 2025, built on the o3 reasoning model; Google launched a feature under the same name within Gemini in late 2024 and expanded it through 2025. Perplexity offered a comparable mode earlier. These tools found rapid adoption for competitive intelligence, academic literature surveys, and due-diligence reporting—tasks that previously required hours of manual effort from knowledge workers.
As of 2026, Deep Research is a standard capability tier offered by major AI platforms, and enterprise deployments routinely augment web-browsing access with retrieval over internal document stores. Persistent challenges include hallucination of sources not actually read, over-reliance on SEO-optimized content, and limited access to paywalled or proprietary databases. Evaluation frameworks for research agent output quality—covering citation accuracy, coverage, and factual correctness—have become an active area of benchmarking.