Accelerate research and development with Co-Scientist agent

Preview

These features are Generative AI Preview offerings, subject to the "Pre-GA Offerings Terms" of the Google Cloud Service Specific Terms. For these Generative AI Preview offerings, Customers may elect to use them for production or commercial purposes, or disclose Generated Output to third-parties, and may process personal data as outlined in the Cloud Data Processing Addendum, subject to the obligations and restrictions described in the agreement under which you access Google Cloud.

The AI/ML Data Location terms also apply to Co-Scientist, as an Agentic AI Service on Gemini Enterprise, subject to its restrictions and those listed in the Google Cloud Platform Services with Data Residency page.

Co-Scientist and AlphaEvolve are Google-developed agents that accelerate research and development processes. These agents leverage Gemini to automate complex scientific reasoning and algorithmic discovery.

Note: Access to these agents is restricted. To request access, contact your Google account team. Your account team provides detailed product documentation once they grant access.

Overview of Co-Scientist agent

Co-Scientist is a multi-agent AI system built on Gemini, designed to act as a virtual scientific collaborator. The system helps researchers uncover original knowledge and formulate novel research hypotheses and experimental proposals. By combining AI reasoning with the scientific method, Co-Scientist enables scientists to navigate literature, synthesize insights across domains, and accelerate biomedical discoveries.

How it works

Co-Scientist is built on a multi-agent architecture and an asynchronous task execution framework to scale test-time computation. This design allows the system to reason, debate, and improve outputs iteratively.

When you provide a research goal in natural language, Co-Scientist coordinates the following specialized agents:

• Generation agent: Explores literature using web search, synthesizes findings, and formulates initial hypotheses.
• Reflection agent: Acts as a peer reviewer to examine the correctness, quality, and safety of generated hypotheses.
• Ranking agent: Evaluates and prioritizes research proposals using an Elo-based tournament.
• Evolution agent: Refines top-ranked hypotheses by grounding them in literature and combining innovative ideas.
• Proximity agent: Organizes tournament matches and deduplicates hypotheses using a proximity graph.
• Meta-review agent: Synthesizes insights from all reviews and debates into a comprehensive research roadmap.

A dedicated Supervisor agent orchestrates the process, allocates resources, and manages the worker queue to maintain a self-improving loop of scientific reasoning.

Use cases

Co-Scientist solves complex problems across multiple scientific disciplines, specifically those requiring deep subject matter expertise and trans-disciplinary insights. Example use cases include:

• Drug repurposing: Identify novel therapeutic indications for approved drugs by analyzing molecular signatures, signaling pathways, and literature-based information.

• Target discovery: Propose new biological components and epigenetic targets for complex diseases and streamline hypothesis selection for experimental validation.

• Mechanistic explanations: Generate hypotheses for intricate problems, such as antimicrobial resistance, by synthesizing fragmented scientific data.

For more information and additional use cases, see the Accelerating scientific breakthroughs with an AI Co-Scientist blog.