Google releases reimagined Gemini Deep Research on Gemini 3 Pro
Google released a reimagined version of its research agent, Gemini Deep Research, on Thursday, coinciding with OpenAI’s announcement of GPT-5.2. The update builds on the Gemini 3 Pro foundation model to enhance factual accuracy and enable developer integration for advanced AI applications.
The new Gemini Deep Research agent retains its ability to generate research reports while introducing expanded functionalities. Developers can now embed Google’s SATA-model research capabilities directly into their own applications. This integration occurs through the newly launched Interactions API, which provides developers with increased control over AI operations as agentic systems become more prevalent in software development.
At its core, the agent processes and synthesizes vast amounts of information efficiently. It manages large context dumps within prompts, allowing it to handle complex data sets without losing coherence. Customers already employ the tool for practical applications, including due diligence processes in business and drug-toxicity safety research in pharmaceuticals, demonstrating its utility in real-world scenarios requiring precise information handling.
Google intends to incorporate the deep-research agent into several of its existing services to broaden accessibility. These include Google Search for improved query resolution, Google Finance for detailed market analysis, the Gemini App for user interactions, and NotebookLM for note-taking and knowledge organization. Such integrations aim to leverage the agent’s strengths across Google’s ecosystem.
The agent’s performance relies heavily on the Gemini 3 Pro model’s design as Google’s most factual foundation model. This model undergoes training specifically to minimize hallucinations, instances where large language models generate inaccurate information. Hallucinations pose a significant risk in long-running, deep-reasoning tasks, where agents make numerous autonomous decisions over extended periods such as minutes or hours. A single hallucinated choice in these sequences can compromise the validity of the entire output, making reduced hallucination rates essential for reliable operation.
To substantiate its advancements, Google developed a new evaluation benchmark named DeepSearchQA. This benchmark assesses AI agents on complex, multi-step information-seeking tasks that mimic real research challenges. Google has made DeepSearchQA available as an open-source resource, enabling the broader AI community to test and compare agent capabilities using standardized metrics.