# About the workshop
LLM agents increasingly collaborate and utilize external tools to address complex user requests. This emerging paradigm demands vast volumes of data for training, testing, reasoning, memory, and execution to ensure accurate and timely responses and actions. Despite the clear benefits, there remains a pressing need for a more rigorous characterization of software engineering constructs to effectively support LLM agents, particularly in areas such as orchestration, data management, knowledge augmentation, and planning. This workshop explores novel solutions to these challenges from the joint perspectives of software engineering and big data.

# Aims
The goal of this workshop is to bring together original, high-quality contributions on software engineering challenges and applied solutions for agentic AI and its synergy with big data, spanning architecture, development, verification, observability, agent-tool protocols, and resource management.

# Topics
Topics of interest include, but are not limited to:
- Agentic big data frameworks and systems.
- Requirements engineering for agentic systems.
- Design and architecture of LLM-based multi-agent systems.
- Development processes and data management in agentic systems.
- Code and other artifact generation assisted by software agents.
- Standardized agent-tool protocols (e.g., MCP, A2A).
- Human-in-the-loop in agentic systems.
- Conversational agents and chatbot frameworks.
- Structured and unstructured data manipulation for agentic recommender systems.
- Reproducibility and traceability of agentic systems development.
- Testing, verification, validation, and LLM-based multi-agent consensus mechanisms.
- Evaluation approaches and frameworks including LLMs as judges and LLM-based qualitative and quantitative metrics.
- Observability, accountability, audit trails, and reproducibility in LLM-based multi-agent systems.
- Empirical studies of multi-agent frameworks (e.g. LangChain, AutoGen, CrewAI).
- Domain-specific applications, including data science, spatial-temporal, and agentic recommender systems.
- Self-adaptation and other self-* properties in the context of agentic AI.
- Real-time or dynamic agentic execution, visualization, and workflows.