# Supporting Dynamic Agentic Workloads: How Data and Agents Interact

arXiv: https://arxiv.org/abs/2512.09548 · HTML: https://mlsanity.com/paper/2512.09548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Theory of Mind for Multi-Agent Collaboration via Large Language Models](https://mlsanity.com/paper/2310.10701/) (2023)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Bao: Learning to Steer Query Optimizers](https://mlsanity.com/paper/2004.03814/) (2020)
- [LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report](https://mlsanity.com/paper/2405.00732/) (2024)
- [Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First](https://mlsanity.com/paper/2509.00997/) (2025)
- [GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching](https://mlsanity.com/paper/2411.05276/) (2024)
- [Robust Markov Decision Processes: A Place Where AI and Formal Methods Meet](https://mlsanity.com/paper/2411.11451/) (2024)
- [vCache: Verified Semantic Prompt Caching](https://mlsanity.com/paper/2502.03771/) (2025)
- [VerdictDB: Universalizing Approximate Query Processing](https://mlsanity.com/paper/1804.00770/) (2018)
- [SentenceKV: Efficient LLM Inference via Sentence-Level Semantic KV Caching](https://mlsanity.com/paper/2504.00970/) (2025)
- [SemShareKV: Efficient KVCache Sharing for Semantically Similar Prompts via Token-Level LSH Matching](https://mlsanity.com/paper/2509.24832/) (2025)
- [Cuttlefish: A Lightweight Primitive for Adaptive Query Processing](https://mlsanity.com/paper/1802.09180/) (2018)
- [Sibyl: Forecasting Time-Evolving Query Workloads](https://mlsanity.com/paper/2401.03723/) (2024)
- [SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads](https://mlsanity.com/paper/2310.14666/) (2023)
- [Incremental Techniques for Large-Scale Dynamic Query Processing](https://mlsanity.com/paper/1902.00585/) (2019)

## Built on by (most cited first, 2 of 2)

- [MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security](https://mlsanity.com/paper/2604.07551/) (2026)
- [AgileLog: A Forkable Shared Log for Agents on Data Streams](https://mlsanity.com/paper/2604.14590/) (2026)

## Abstract (arXiv)

The rise of multi-agent systems powered by large language models (LLMs) and specialized reasoning agents exposes fundamental limitations in today's data management architectures. Traditional databases and data fabrics were designed for static, well-defined workloads, whereas agentic systems exhibit dynamic, context-driven, and collaborative behaviors. Agents continuously decompose tasks, shift attention across modalities, and share intermediate results with peers - producing non-deterministic, multi-modal workloads that strain conventional query optimizers and caching mechanisms. We propose an Agent-Centric Data Fabric, a unified architecture that rethinks how data systems serve, optimize, coordinate, and learn from agentic workloads. To achieve this we exploit the concepts of attention-guided data retrieval, semantic micro-caching for context-driven agent federations, predictive data prefetching and quorum-based data serving. Together, these mechanisms enable agents to access representative data faster and more efficiently, while reducing redundant queries, data movement, and inference load across systems. By framing data systems as adaptive collaborators, instead of static executors, we outline new research directions toward behaviorally responsive data infrastructures, where caching, probing, and orchestration jointly enable efficient, context-rich data exchange among dynamic, reasoning-driven agents.