For Provenance and with Provenance: The Role of Provenance Data in Agentic Workflows.

For Provenance and with Provenance: The Role of Provenance Data in Agentic Workflows.

Renan Souza (Oak Ridge National Laboratory)

December 10, 2025
11:00-11:30 PST / 14:00-14:30 EST / 20:00-20:30 CEST

The convergence of HPC, AI, and edge instrumentation is enabling "Autonomous Science," a paradigm shift capable of compressing discovery cycles from years to months. However, shifting to agent-driven workflows introduces risks regarding non-determinism and "dataflow contamination," which threaten scientific reproducibility. In this presentation, we argue that robust provenance data management is the critical enabler for trustworthy autonomous systems.

We introduce a dual framework: "Provenance of Agents," which enforces accountability by systematically capturing agent decisions for root cause analysis, and "Provenance with Agents," which leverages Large Language Models (LLMs) as interfaces to democratize access to complex runtime data. Showcasing the Flowcept architecture and real-world applications in computational chemistry and adaptive additive manufacturing at Oak Ridge National Laboratory, we demonstrate how provenance safeguards the scientific method within autonomous loops. By ensuring transparency and enabling real-time human steering across the Edge-Cloud-HPC continuum , this approach removes manual bottlenecks, significantly reducing time-to-solution and accelerating the pace of trusted scientific discovery.

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About the Authors

Renan Souza

Renan Souza
Research Staff

Renan Souza earned his Ph.D., M.Sc., and B.Sc. in Computer Science (2009-2019) from the Federal University of Rio de Janeiro (UFRJ). Since 2022, he has been a researcher and software engineer at Oak Ridge National Laboratory, after spending seven years at IBM. He was a visiting scientist at INRIA, France, during his Ph.D. and, during his B.Sc., studied abroad at Missouri State University and interned at SLAC National Laboratory. Active in engineering, research, and technical leadership since 2010, he has authored 50+ peer-reviewed papers in leading venues and holds 10+ USPTO patents. His current focus is on designing and building scalable systems to support responsible and trustworthy AI workflows.