Overview of the RFI
The Defense Advanced Research Projects Agency (DARPA), through its Biological Technology Office (BTO), has released a Request for Information (RFI) titled “Advancing Autonomous Science for Biological Applications” (Special Notice DARPA-SN-26-96). Through this market research effort, DARPA is seeking feedback from industry, academia, and other stakeholders to better understand the current state of autonomous science in biological domains and to identify where targeted investment could have the greatest impact in service to national security and public benefit.
Autonomous science, the integration of artificial intelligence, robotics, and automation into scientific discovery and experimentation, has evolved rapidly from small-scale, bespoke systems to large, flexible facilities capable of addressing complex experimental challenges at scale. While recent advances in foundation models, agentic AI, and general-purpose robotics have accelerated progress, many approaches remain in early stages of maturity, and the field continues to search for transformative applications that demonstrate unique advantages over conventional methods.
Technical Areas of Interest
DARPA is particularly interested in responses that address Informatics Technologies, Physical Technologies, or Both. Responses must cover at least one of the following five research areas:
- Current State of the Art: Key existing and emerging enabling technologies broadly transferrable across scientific workflows, including metrics of success such as throughput, reproducibility, power usage, continuous runtime, and cost per assay.
- Technical and Operational Bottlenecks: Challenges to rapid development and exchange of new autonomous workflows, interoperability between labs, and exchange of data and protocols.
- High-Impact Application Areas: Problems of national interest where autonomous science can achieve results infeasible via conventional approaches, including commercialization strategies and metrics for demonstrating improvement.
- Safety and Security Considerations: Risks in the operation of autonomous labs, current safeguards including system-level security and traceability approaches, and enabling technologies such as AI digital twins, simulation, and formal verification to ensure safe and secure operation.
- Human-AI Hybrid Workflows: Opportunities, barriers, and enabling technologies for productive interaction between human operators and autonomous agents (including nationally significant applications achievable only through human-augmented autonomous facilities) and metrics to demonstrate the value of human-machine partnering over unassisted human or purely autonomous systems.
DARPA welcomes responses from all capable sources, including private and public companies, individuals, universities, university-affiliated research centers, not-for-profit research institutions, and U.S. Government-sponsored labs. Respondents are encouraged to submit non-proprietary information, and classified information must not be included under any circumstances.
Key Dates and Submission Information
- RFI Issued: June 23, 2026
- Response Deadline: July 22, 2026 at 5:00 p.m. ET
Responses are limited to no more than seven pages using 12-point font and 1-inch margins, submitted as an unprotected Microsoft Word document or PDF. Responses should include a cover page, up to three pages of technical content, and a references page, with optional summary slides consisting of 1 summary slide and 1 technical description slide.
Submissions should be emailed to [email protected], referencing DARPA-SN-26-96 in both the subject line and body of the email.
Why This Matters
This RFI provides valuable insight into DARPA’s interest in autonomous biomedical science, an area with broad implications for drug discovery, biosurveillance, pandemic preparedness, and national defense. For developers of AI-driven laboratory platforms, autonomous robotics systems, or advanced bioengineering workflows, this is an opportunity to help shape future federal priorities at the intersection of biology and machine intelligence.
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