DBScholar

Back to papers

Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First

Summary: LLM agents to become dominant data workload; "agentic speculation"—high-throughput exploratory/solution iterations—creates scale, heterogeneity, redundancy, and steerability challenges. Proposes an agent-first DB architecture and research agenda (interfaces, processing, agentic memory) to natively support them. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
593
Venue
CIDR
Year
2026
Pagerank
5.9425753e-05
Overall Rank
6,210 | 57.40%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{liu_cidr26,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '26},
        title = {{Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Liu, Shu and Ponnapalli, Soujanya and Shankar, Shreya and Zeighami, Sepanta and Zhu, Alan and Agarwal, Shubham and Chen, Ruiqi and Suwito, Samion and Yuan, Shuo and Stoica, Ion and Zaharia, Matei and Cheung, Alvin and Crooks, Natacha and Gonzalez, Joseph E. and Parameswaran, Aditya G.},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,228 Efficient LLM Serving for Agentic Workflows: A Data Systems Perspective 2026 SIGMOD 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers