Large language models (LLMs) and autonomous agents are reshaping how data systems are designed and used. Modern AI applications increasingly operate as compound systems that integrate reasoning, retrieval, planning, and tool use over heterogeneous and continuously evolving data ecosystems. These systems must interact with structured and unstructured data, knowledge graphs, multimodal content, and external tools while balancing trade-offs in cost, latency, scalability, robustness, governance, and trust. Effectively deploying agentic AI in such environments requires a systems-driven approach grounded in data management principles and human oversight. Human-in-the-loop methodologies remain central to alignment, evaluation, debugging, and lifecycle management of evolving agent workflows. Ensuring that agents remain reliable, interpretable, and aligned with human intent is as much a data systems challenge as it is a modeling challenge. DASHSys is a full-day workshop dedicated to advancing the foundations, architectures, and evaluation of data-centric agentic systems.
DASHSys invites original research contributions, system papers, position papers, and real-world system reports at the intersection of:
We welcome submissions that advance the theory, systems, and practice of building data-aware, agent-driven, and human-aligned AI systems.
Topics include (but are not limited to):
Submissions should present original results and substantial new work not currently under review or published elsewhere. Manuscripts must be prepared following the same rules as VLDB conference papers. Papers must be submitted via the workshop's submission system in PDF format. Demo-oriented submissions are strongly encouraged. Artifact availability (code, datasets, system demos) is highly encouraged.
DASHSys will follow a double-anonymous review process. Each paper will be evaluated based on relevance, originality, technical quality, and clarity. Reviewers will be instructed to provide constructive feedback. Accepted papers will appear in the official VLDB workshop proceedings.
The Microsoft CMT service is used for managing the peer-reviewing process for this conference. This service is provided for free by Microsoft and they bear all expenses, including costs for Azure cloud services as well as for software development and support.
A competition where participants build production-grade agentic systems for performing data centric tasks spanning complex SQL queries and API calls.
We're looking for systems that go beyond demos: robust prompt and system designs that generalize across models, architectures for reliable agent-driven data systems, effective orchestration of SQL and API workflows, and practical approaches to efficiency and scalability. Deliverables include a metadata JSON with query-specific context, a filled system prompt and system prompt template, an agent output trajectory JSON, and a zipped source code archive.
Submissions are ranked on two dimensions:
Winners receive monetary prizes.
