
Be able to:
Articulate why scientific research is inherently agentic and why the agent loop (plan → act → observe → revise) maps naturally onto the scientific method
Describe the emerging "scientific agent stack" and connect it to architectures they have already built (ReAct, tool calling, MCP, RAG, A2A)
Identify concrete agentic AI systems across the biological sciences (drug discovery, protein engineering) and physical sciences (materials discovery, autonomous laboratories, chemistry)
Explain how the Ai2 tool ecosystem (Semantic Scholar, OpenScholar, ScholarQA, Asta) provides the knowledge infrastructure that scientific agents depend on
Analyze the JFC framework (Moreno et al., 2026) as an integrative case study that combines autonomous agent execution, literature RAG, and multi-agent review to perform experimental high-energy physics
Critically evaluate the challenges of deploying agents in high-stakes scientific domains: hallucination, verification, reproducibility, and the limits of autonomy
Work on your final project
Agentic AI for science lesson plan
Agarwal, Dhruv et al (2025), AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise, NeurIPS 2025, https://arxiv.org/abs/2507.00310
Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak, Dolores Garcia, Philip Harris (2026). AI Agents Can Already Autonomously Perform Experimental High Energy Physics. https://inspirehep.net/literature/3132489
HEPTAPOD: (High Energy Physics (HEP) Toolkit for Agentic Planning, Orchestration and Deployment) is a publicly available AI agent orchestration framework for particle physics simulation and analysis workflows built on the Orchestral AI Framework. HEPTAPOD Paper: [https://arxiv.org/abs/2512.15867]
Github Repo: https://github.com/tonymenzo/heptapod
Orchestral AI: https://orchestral-ai.com