Speaker
Description
Scientific decision-making in complex systems requires interactive systems, such as digital twins, that can combine domain knowledge, data-intensive workflows, and transparent computational methods. We present an approach for building AI agents that support well-grounded, data-based decisions by embedding conversational capabilities directly into scientific web applications. The approach combines the Data Analytics Software Framework (DASF), a secure message-broker-based remote procedure call system, with large-language-models, to expose scientific tools, workflows, and data products as AI agents.
Rather than providing a detached chatbot beside a domain application, DASF-based AI agents operate within the application context. They can trigger analyses, parameterize workflows, orchestrate computations close to data and high-performance computing resources, and return results as domain-native outputs such as maps, plots, tables, and dashboards. This enables conversational interaction while preserving scientific traceability, institutional security requirements, and established user interfaces.
As an exemplary implementation, we demonstrate the concept with iseapower.hereon.de, a decision-support tool for offshore wind farm maintenance. In this setting, the AI agent assists users in exploring maintenance-relevant scenarios, initiating analytical workflows, and interpreting results in the operational context of the platform. DASF enables secure and asynchronous execution without exposing infrastructure through inbound internet-facing ports, while the conversational agent provides lightweight tool access.
The resulting architecture turns conversation into an additional interaction modality for scientific decision-support systems. The approach generalizes to knowledge-transfer scenarios in which robust, transparent, and context-aware AI support is needed for well-grounded science-based decisions.