Scientific discovery is rapidly evolving through the integration of artificial intelligence, robotics, and autonomous systems. However, many autonomous laboratories still operate in isolation, limiting collaboration and slowing progress. This roadmap outlines a community-driven strategy to connect capabilities across institutions, standardize interfaces for interoperability, and enable intelligent orchestration of experiments and data. It identifies critical dimensions for building a cohesive national infrastructure that supports distributed, automated, and reproducible science. The goal is to accelerate the pace of discovery while expanding access to advanced scientific tools and methods.
Instrument and Cyberinfrastructure Integration
Interconnecting autonomous laboratories requires agents to operate diverse scientific instruments and computational resources across institutional boundaries. This integration supports seamless workflows from data collection to analysis, enabling rapid, reproducible scientific discovery. Key challenges include heterogeneity of instruments, lack of standardized interfaces, and coordination of distributed resources.
- M1. Establish common integration interfaces for scientific instruments with vendor-agnostic hardware abstraction layers and API development via an Instrument API Consortium.
- M2. Demonstrate end-to-end autonomous workflows across institutions for seamless experimental and computational resource orchestration through secure multi-domain cyberinfrastructure networks.
- M3. Deploy federated cyberinfrastructure with standardized frameworks, fault-tolerant coordination mechanisms, and adaptive resource management with zero-trust security and physics-aware digital twins for workflow validation.
- M4. Develop a scalable national framework supporting heterogeneous instruments for near real-time data flows with self-describing instruments, automated calibration, and human-in-the-loop override capabilities.
Agent-Driven Data Management
This approach shifts from passive data storage to intelligent systems where autonomous agents manage the full data lifecycle. Agents curate, validate, and share data across facilities while enforcing FAIR principles. Key challenges include schema variability, data quality assurance, privacy concerns, and the need for real-time processing.
- M5. Develop AI-driven metadata systems with automated annotation of experimental data in multiple domains, achieving high accuracy without human intervention.
- M6. Deploy federated data mesh architecture with common APIs, cross-institutional discovery capabilities, and autonomous FAIR data governance.
- M7. Implement near real-time data processing infrastructure supporting high-velocity scientific streams with automated quality assessment, provenance tracking, and regulatory compliance frameworks.
AI Agent-Driven Autonomous Orchestration
Intelligent agents coordinate experimental decisions using AI methods grounded in scientific knowledge. These agents integrate domain expertise, real-time data, and model-based reasoning to plan and execute workflows. Challenges include the probabilistic nature of AI models, ensuring scientific validity, and orchestrating across distributed, asynchronous systems.
- M8. Demonstrate hierarchical architectures in which LLM agents orchestrate traditional methods through domain-specific scientific interfaces, achieving significant speedups and experimental accuracy.
- M9. Deploy a knowledge integration system with multiple facilities, propagating insights in real time to reduce experimental requirements and increase scientist approval of reasoning traces.
Interoperable Agent Communication Interfaces and Standards
To enable coordination between agents across institutions, standardized communication protocols are essential. These interfaces support asynchronous, secure, and fault-tolerant messaging while ensuring semantic interoperability across domains. Key challenges include diverse instrument protocols, security requirements, and reliable coordination at scale.
- M10. Deploy containerized agent microservices with standardized gRPC and AMQP communication protocols across multiple laboratory facilities, demonstrating cross-vendor instrument control and federated identity integration.
- M11. Develop zero-trust communication infrastructure supporting autonomous agent coordination with sub-second latency, automatic failover, and continuous authentication.
- M12. Demonstrate self-discovering agent networks using DNS-SD and distributed service registries, enabling dynamic reconfiguration and capability negotiation in geographically distributed facilities.
Education and Workforce Development
Preparing the next generation of scientists to work with autonomous systems requires rethinking education. Training must integrate AI and robotics with core scientific skills and emphasize human-AI collaboration, ethical reasoning, and system oversight. Key challenges include curriculum development, faculty expertise, and equitable access to hands-on learning.
- M13. Launch a national autonomous science education consortium that integrates NSF AI Institutes and DOE SciDAC programs with standardized autonomous laboratory collaboration curricula.
- M14. Deploy educational infrastructure including immersive virtual laboratory environments, industry-academic partnership programs, and assessment methods for evaluating human-AI collaboration skills.