Nathan Fulton
Nathan Fulton is a manager at IBM Research. He is an expert in large language models, formal verification, and reinforcement learning. Nathan earned bachelors degree from Carthage College in Computer Science and Mathematics, and a Ph.D. from Carnegie Mellon University's Computer Science Department. During his PhD studies, Nathan was a member of André Platzer's Logical Systems Lab and a core developer of the KeYmaera X theorem prover for hybrid systems. Nathan has previously worked as a Senior Applied Scientist at Amazon Web Services and as a Research Scientist at the MIT-IBM AI Lab.
Session
Agentic frameworks make it easy to build and deploy compelling demos. But building robust systems that use LLMs is difficult because of inherent environmental non-determinism. Each user is different, each request is different; the very flexibility that makes LLMs feel magical in-the-small also makes agents difficult to wrangle in-the-large.
Developers who have built large agentic-like systems know the pain. Exceptional cases multiply, prompt libraries grow, instructions are co-mingled with user input. After a few iterations, an elegant agent evolves into a big ball of mud.
This hands-on tutorial introduces participants to Mellea, an open-source Python library for writing structured generative programs. Mellea puts the developer back in control by providing the building blocks needed to circumscribe, control, and mediate essential non-determinism.