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+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.
+
+<br /><br />
+
+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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