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14+ The convergence of HPC, AI, and edge instrumentation is enabling "Autonomous
15+ Science," a paradigm shift capable of compressing discovery cycles from years to
16+ months. However, shifting to agent-driven workflows introduces risks regarding
17+ non-determinism and "dataflow contamination," which threaten scientific
18+ reproducibility. In this presentation, we argue that robust provenance data
19+ management is the critical enabler for trustworthy autonomous systems.
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23+ We introduce a dual framework: "Provenance of Agents," which enforces accountability
24+ by systematically capturing agent decisions for root cause analysis, and
25+ "Provenance with Agents," which leverages Large Language Models (LLMs) as
26+ interfaces to democratize access to complex runtime data. Showcasing the Flowcept
27+ architecture and real-world applications in computational chemistry and adaptive
28+ additive manufacturing at Oak Ridge National Laboratory, we demonstrate how
29+ provenance safeguards the scientific method within autonomous loops. By ensuring
30+ transparency and enabling real-time human steering across the Edge-Cloud-HPC
31+ continuum , this approach removes manual bottlenecks, significantly reducing
32+ time-to-solution and accelerating the pace of trusted scientific discovery.
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