Consensus Launches GPT-5 Powered 'Scholar Agent' to Accelerate Scientific Research
Executive Summary
Research assistant platform Consensus has launched Scholar Agent, a new multi-agent system designed to automate complex scientific research workflows. Built on OpenAI's GPT-5 and the Responses API, the system plans, reads, and synthesizes evidence from over 220 million academic papers to answer user questions in minutes. This initiative aims to drastically reduce the time researchers spend finding and interpreting existing knowledge, thereby accelerating the path to new discoveries.
Key Takeaways
* Product: Scholar Agent is a multi-agent system that mirrors human research processes.
* Technology Stack: It is built using OpenAI's GPT-5 for long-context reasoning and the Responses API for efficient multi-agent routing.
* Agentic Architecture: The workflow is divided among four specialized agents: a Planning Agent, Search Agent, Reading Agent, and Analysis Agent. This modular approach is designed to improve accuracy and minimize hallucinations.
* Verifiability: The system provides a "research context pack" with every answer, grounding all claims in cited, peer-reviewed literature. It will refuse to answer a question if it cannot find sufficient high-quality evidence.
* Target Audience: The platform focuses on a direct-to-researcher model, targeting students, academics, private researchers, and clinicians. Consensus recently launched a 'Medical Mode' specifically for medical practitioners.
Strategic Importance
This launch positions Consensus as a sophisticated "agentic assistant" rather than a simple search tool, showcasing a high-stakes application for advanced AI in a specialized professional domain. It also serves as a key proof point for the capabilities of OpenAI's GPT-5 and Responses API in building reliable, complex, and verifiable systems.