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[2602.17902] El Agente Gráfico: Structured Execution Graphs for Scientific Agents
Summary
The paper introduces El Agente Gráfico, a framework that enhances scientific workflows by integrating LLMs with structured execution graphs, improving decision-making and context management.
Why It Matters
As scientific research increasingly relies on automation, this framework addresses the challenges of integrating diverse computational tools, ensuring consistency and auditability in complex workflows. It represents a significant advancement in the use of AI for scientific applications.
Key Takeaways
- El Agente Gráfico integrates LLMs with structured execution graphs.
- The framework enhances decision-making and context management in scientific workflows.
- It supports provenance tracking and efficient tool orchestration.
- The system demonstrates robust performance in complex, multi-step computations.
- Knowledge graphs serve as memory and reasoning substrates for various applications.
Computer Science > Artificial Intelligence arXiv:2602.17902 (cs) [Submitted on 19 Feb 2026] Title:El Agente Gráfico: Structured Execution Graphs for Scientific Agents Authors:Jiaru Bai, Abdulrahman Aldossary, Thomas Swanick, Marcel Müller, Yeonghun Kang, Zijian Zhang, Jin Won Lee, Tsz Wai Ko, Mohammad Ghazi Vakili, Varinia Bernales, Alán Aspuru-Guzik View a PDF of the paper titled El Agente Gr\'afico: Structured Execution Graphs for Scientific Agents, by Jiaru Bai and Abdulrahman Aldossary and Thomas Swanick and Marcel M\"uller and Yeonghun Kang and Zijian Zhang and Jin Won Lee and Tsz Wai Ko and Mohammad Ghazi Vakili and Varinia Bernales and Al\'an Aspuru-Guzik View PDF Abstract:Large language models (LLMs) are increasingly used to automate scientific workflows, yet their integration with heterogeneous computational tools remains ad hoc and fragile. Current agentic approaches often rely on unstructured text to manage context and coordinate execution, generating often overwhelming volumes of information that may obscure decision provenance and hinder auditability. In this work, we present El Agente Gráfico, a single-agent framework that embeds LLM-driven decision-making within a type-safe execution environment and dynamic knowledge graphs for external persistence. Central to our approach is a structured abstraction of scientific concepts and an object-graph mapper that represents computational state as typed Python objects, stored either in memory or persisted in an extern...