Google’s new Deep Research and Deep Research Max agents can search the web and your private data

Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product's debut, launching two new agents — Deep Research and Deep Research Max — that for the first time allow developers to fuse open web data with proprietary enterprise information through a single API call, produce native charts and infographics inside research reports, and connect to arbitrary third-party data sources through the Model Context Protocol (MCP).

The release, built on Google's Gemini 3.1 Pro model, marks an inflection point in the rapidly intensifying race to build AI systems that can autonomously conduct the kind of exhaustive, multi-source research that has traditionally consumed hours or days of human analyst time. It also represents Google's clearest bid yet to position its AI infrastructure as the backbone for enterprise research workflows in finance, life sciences, and market intelligence — industries where the stakes of getting information wrong are extraordinarily high.

"We are launching two powerful updates to Deep Research in the Gemini API, now with better quality, MCP support, and native chart/infographics generation," Google CEO Sundar Pichai wrote on X. "Use Deep Research when you want speed and efficiency, and use Max when you want the highest quality context gathering & synthesis using extended test-time compute — achieving 93.3% on DeepSearchQA and 54.6% on HLE."

Both agents are available starting today in public preview via paid tiers of the Gemini API, accessible through the Interactions API that Google first introduced in December 2025.

Why Google built two research agents instead of one

The launch introduces a tiered architecture that reflects a fundamental tension in AI agent design: the tradeoff between speed and thoroughness.

Deep Research, the standard tier, replaces the preview agent Google released in December and is optimized for low-latency, interactive use cases. It delivers what Google describes as significantly reduced latency and cost at higher quality levels compared to its predecessor. The company positions it as ideal for applications where a developer wants to embed research capabilities directly into a user-facing interface — think a financial dashboard that can answer complex analytical questions in near-real time.

Deep Research Max occupies the opposite end of the spectrum. It leverages extended test-time compute — a technique where the model spends more computational cycles iteratively reasoning, searching, and refining its output before delivering a final report. Google designed it for asynchronous, background workflows: the kind of task where an analyst team kicks off a batch of due diligence reports before leaving the office and expects exhaustive, fully sourced analyses waiting for them the next morning.

The Google DeepMind team framed the distinction on X: "Deep Research: Optimized for speed and efficiency. Perfect for interactive apps needing quicker responses. Deep Research Max: It uses extra time to search and reason. Ideal for exhaustive context gathering and tasks happening in the background."

"Deep Research was our first hosted agent in the API and has gained a ton of traction over the last 3 months, very excited for folks to test out the new agents and all the improvements, this is just the start of our agents journey," Logan Kilpatrick, who leads developer relations for Google's AI efforts, wrote on X.

MCP support lets the agents tap into private enterprise data for the first time

Perhaps the most consequential feature in today's release is the addition of Model Context Protocol support, which transforms Deep Research from a sophisticated web research tool into something more closely resembling a universal data analyst.

MCP , an emerging open standard for connecting AI models to external data sources, allows Deep Research to securely query private databases, internal document repositories, and specialized third-party data services — all without requiring sensitive information to leave its source environment. In practical terms, this means a hedge fund could point Deep Research at its internal deal-flow database and a financial data terminal simultaneously, then ask the agent to synthesize insights from both alongside publicly available information from the web.

Google disclosed that it is actively collaborating with FactSet, S&P, and PitchBook on their MCP server designs, a signal that the company is pursuing deep integration with the data providers that Wall Street and the broader financial services industry already rely on daily. The goal, according to the blog post authored by Google DeepMind product managers Lukas Haas and Srinivas Tadepalli, is to "let shared customers integrate financial data offerings into workflows powered by Deep Research, and to enable them to realize a leap in productivity by gathering context using their exhaustive data universes at lightning speed."

This addresses one of the most persistent pain points in enterprise AI adoption: the gap between what a model can find on the open internet and what an organization actually needs to make decisions. Until now, bridging that gap required significant custom engineering. MCP support, combined with Deep Research's autonomous browsing and reasoning capabilities, collapses much of that complexity into a configuration step. Developers can now run Deep Research with Google Search, remote MCP servers, URL Context, Code Execution, and File Search simultaneously — or turn off web access entirely to search exclusively over custom data. The system also accepts multimodal inputs including PDFs, CSVs, images, audio, and video as grounding context.

Native charts and infographics turn AI reports into stakeholder-ready deliverables

The second headline feature — native chart and infographic generation — may sound incremental, but it addresses a practical limitation that has constrained the usefulness of AI-generated research outputs in professional settings.

Previous versions of Deep Research produced text-only reports. Users who needed visualizations had to export the data and build charts themselves, a friction point that undermined the promise of end-to-end automation. The new agents generate high-quality charts and infographics inline within their reports, rendered in HTML or Google's Nano Banana format, dynamically visualizing complex datasets as part of the analytical narrative.

"The agent generates HTML charts and infographics inline with the report. Not screenshots. Not suggestions to 'visualize this data.' Actual rendered charts inside the markdown output," noted AI commentator Shruti Mishra on X, capturing the practical significance of the change.

For enterprise users — particularly those in finance and consulting who need to produce stakeholder-ready deliverables — this transforms Deep Research from a tool that accelerates the research phase into one that can potentially produce near-final analytical products. Combined with a new collaborative planning feature that lets users review, guide, and refine the agent's research plan before execution, and real-time streaming of intermediate reasoning steps, the system gives developers granular control over the investigation's scope while maintaining the transparency that regulated industries demand.

How Deep Research evolved from a consumer chatbot feature to enterprise platform infrastructure

Today's release crystallizes a strategic narrative Google has been building for months: Deep Research is not merely a consumer feature but a piece of infrastructure that powers multiple Google products and is now being offered to external developers as a platform.

The blog post explicitly notes that when developers build with the Deep Research agent, they tap into "the same autonomous research infrastructure that powers research capabilities within some of Google's most popular products like Gemini App, NotebookLM, Google Search and more."