Agents Need Data: Building Systems for Collaborative Intelligence

by

Author: Denis Avetisyan

As reasoning agents become increasingly prevalent, existing data architectures struggle to keep pace with their dynamic needs.

This review explores an Agent-Centric Data Fabric and techniques like attention-guided retrieval, semantic caching, and quorum-based serving to optimize data access for adaptive, agentic systems.

Conventional data architectures struggle to accommodate the dynamic, context-driven behaviors emerging from multi-agent systems powered by large language models. This paper, ‘Supporting Dynamic Agentic Workloads: How Data and Agents Interact’, introduces an Agent-Centric Data Fabric-a novel architecture designed to optimize data access and coordination for these agentic workloads. By leveraging techniques like attention-guided retrieval and semantic caching, we demonstrate substantial improvements in efficiency and reduced redundancy within agent collaborations. Could this adaptive approach pave the way for truly behaviorally responsive data infrastructures capable of supporting increasingly complex reasoning-driven agents?

The Inevitable Erosion of Static Systems

Conventional data architectures, designed for static queries and predictable data flows, are increasingly challenged by the demands of agentic workloads. These systems often rely on centralized data stores and rigid schemas, creating bottlenecks when multiple reasoning agents require concurrent access and modification of information. The collaborative nature of these workloads-where agents decompose tasks, share intermediate results, and dynamically adapt to changing contexts-exacerbates these issues. This leads to inefficiencies as agents spend valuable processing time waiting for data, hindering their ability to respond effectively to complex, real-time challenges. Consequently, the potential of multi-agent systems is limited by the underlying data infrastructure’s inability to support the fluid and interactive requirements of these novel applications.

Agentic workloads, characterized by autonomous agents collaborating to achieve goals, present unique challenges to current computing systems. Unlike traditional, static applications, these systems require infrastructure capable of dynamically adapting to shifting circumstances and intricate agent-to-agent communication. The very nature of these workloads involves constant negotiation, task decomposition, and the integration of information from diverse sources – demanding real-time data access and processing. Successfully supporting such interactions necessitates a move beyond pre-defined data pipelines to systems that can understand context, manage dependencies, and facilitate seamless information exchange between agents as the task evolves. This demands not merely faster processing, but a fundamental rethinking of how data is structured, accessed, and utilized within a highly interactive, autonomous environment.

Current data systems often prove inadequate when faced with agentic workloads because they are built on assumptions of static queries and pre-defined data schemas. These systems struggle with the iterative nature of task decomposition, where agents continuously break down larger goals into smaller, dynamically generated sub-tasks. This creates a mismatch: agents require immediate access to evolving data streams to inform their next steps, yet traditional architectures necessitate lengthy data retrieval processes and rigid data structures. Consequently, agents experience significant latency, hindering their ability to react to changing circumstances and collaborate effectively. The inability to seamlessly integrate real-time data access with continuous task breakdown limits the potential for complex reasoning and adaptive behavior in these emerging agentic systems.

Realizing the full capabilities of reasoning agents necessitates a departure from conventional data architectures, which were not designed to accommodate the fluid and collaborative nature of agentic workloads. Current systems often struggle with the dynamic decomposition of tasks into smaller, interconnected steps and the subsequent need for immediate, contextualized data access as agents interact and refine their understanding. This calls for a foundational reimagining of how data is structured, accessed, and shared – moving beyond static datasets to embrace a more reactive and interconnected approach. A successful paradigm shift will prioritize real-time responsiveness, seamless inter-agent communication, and the ability to adapt to evolving task requirements, ultimately enabling agents to tackle increasingly complex challenges with greater efficiency and autonomy.

Architecting for Agency: A New Data Fabric

The Agent-Centric Data Fabric is designed as a consolidated architectural framework specifically for managing and enhancing agentic workloads. This unified approach addresses the requirements of serving data to agents, optimizing data access patterns for performance, coordinating data interactions between multiple agents, and enabling learning capabilities through data analysis. By integrating these functions into a single architecture, the fabric aims to overcome the limitations of traditional, siloed data management systems when applied to increasingly complex agent-based applications. This consolidation allows for streamlined data pipelines, reduced operational overhead, and improved scalability for agentic systems.

Attention-guided data retrieval and predictive data prefetching are employed within the Agent-Centric Data Fabric to proactively address agent data requirements and reduce operational latency. Attention mechanisms analyze agent requests and historical data access patterns to identify relevant data subsets, prioritizing retrieval based on predicted importance. Predictive prefetching anticipates future data needs by leveraging these patterns, proactively loading data into faster access tiers – such as memory or solid-state drives – before a request is even made. This combined approach minimizes data retrieval time by reducing the need for repeated queries and disk I/O, thereby improving the responsiveness of agentic workloads and enabling real-time decision-making.

Semantic micro-caching optimizes data access by storing data based on its underlying meaning, or semantics, rather than solely on the specific query used to retrieve it. This approach utilizes techniques like knowledge graphs and vector embeddings to represent data and queries in a semantic space, allowing the system to identify and serve relevant data even when the exact query terms differ. By caching data associated with a particular concept or entity, redundant lookups for synonymous or related information are significantly reduced, minimizing latency and improving overall system efficiency. This contrasts with traditional caching methods that rely on exact string matching, which are vulnerable to variations in phrasing or terminology.

Quorum-based data serving addresses the challenges of data consistency and redundancy in multi-agent systems by requiring a defined subset – the quorum – of agents to verify data before it is considered valid. This approach avoids reliance on a single point of failure and ensures data accuracy even with agent failures or inconsistencies. Specifically, data is not served until confirmation from at least $n$ out of $N$ agents is received, where $n$ is a configurable threshold and $N$ is the total number of agents responsible for a given data element. This minimizes redundant data storage as agents do not individually replicate all data, but instead contribute to the quorum verification process. The size of the quorum directly impacts the balance between consistency, availability, and performance; larger quorums provide stronger consistency but may increase latency, while smaller quorums prioritize availability at the cost of potential inconsistencies.

The Inevitable Cost of Efficiency

Cost-based optimization (CBO) and progressive optimization are core components of the system’s query processing engine. CBO utilizes statistical data about data distribution and system resources to determine the most efficient execution plan for a given query, minimizing estimated resource usage – including I/O, CPU, and memory. Progressive optimization builds upon this by iteratively refining the execution plan during query execution, allowing for dynamic adjustments based on runtime data and intermediate results. This approach contrasts with traditional optimization techniques by enabling the system to adapt to changing data characteristics and workload demands, resulting in reduced latency and improved throughput, particularly for complex queries involving large datasets.

The system actively monitors and manages Input/Output (I/O) and Central Processing Unit (CPU) usage to optimize resource allocation during query processing. Minimizing I/O operations, such as disk access, directly reduces latency and improves response times. Similarly, efficient CPU utilization prevents bottlenecks and allows for greater concurrency, increasing the number of queries processed per unit time. This optimization extends to workload scheduling, prioritizing tasks and allocating resources dynamically based on real-time demand, ultimately maximizing throughput and reducing overall operational costs.

Adaptive query processing and incremental query processing optimize performance by modifying query execution plans during runtime based on observed data characteristics and system load. Adaptive processing allows the system to switch between different join algorithms, reorder joins, or select alternative data access paths without requiring a full query rewrite. Incremental processing builds upon this by materializing intermediate results and reusing them for subsequent requests, reducing redundant computation. These techniques are particularly effective in scenarios with varying data distributions or workloads, as they allow the system to dynamically adjust to changing conditions and avoid suboptimal plans determined at compile time, thereby minimizing latency and maximizing throughput.

The system architecture is specifically designed to mitigate the challenges of token cost and inference latency inherent in scaling large language models (LLMs). Token cost, directly impacting operational expenditure, is addressed through efficient data handling and minimized token usage during processing. Inference latency, the time required to generate a response, is reduced via optimized execution paths and parallel processing capabilities. These features are critical for LLM deployments requiring high throughput and low response times, enabling cost-effective scaling to accommodate increasing user demand and model complexity. Careful consideration of these factors allows for predictable and manageable resource utilization when deploying and serving LLMs.

Beyond Pattern Matching: Agents as True Reasoners

The Agent-Centric Data Fabric represents a significant advancement in leveraging the potential of large language models (LLMs) for complex problem-solving. This architecture isn’t simply about feeding data to an LLM; it’s about creating a dynamic ecosystem where the LLM, functioning as a reasoning agent, can actively access, process, and interpret information. By meticulously structuring data access and optimizing processing speeds, the fabric removes bottlenecks that traditionally hinder LLM performance. This allows the agent to move beyond simple pattern recognition and engage in more nuanced, multi-step reasoning. The seamless integration fosters a feedback loop, where the LLM’s queries refine data retrieval, leading to more accurate conclusions and increasingly sophisticated insights. Ultimately, the fabric transforms LLMs from powerful text generators into truly intelligent agents capable of tackling complex challenges with greater efficiency and reliability.

The Agent-Centric Data Fabric dramatically enhances the performance of reasoning agents by prioritizing both swift data retrieval and streamlined processing. Traditional systems often bottleneck when faced with complex queries, forcing agents to expend valuable time locating and preparing information; this fabric circumvents such limitations through optimized data pathways and efficient computational resources. Consequently, agents can dedicate more cycles to actual reasoning, leading to significantly faster task completion and a marked increase in accuracy. This isn’t merely about speed, however; the optimized processing also allows agents to handle larger, more intricate datasets, tackling problems previously beyond their reach and opening doors to more nuanced and reliable conclusions.

The Agent-Centric Data Fabric distinguishes itself through its support for speculative querying, a process enabling reasoning agents to move beyond reactive data retrieval. Rather than waiting for specific requests, the fabric allows agents to proactively explore interconnected data sources, formulating hypotheses and seeking evidence before a direct question is posed. This anticipatory approach leverages the fabric’s architecture to pre-fetch and analyze potentially relevant information, significantly reducing latency and revealing previously hidden insights. By essentially ‘thinking ahead’, the system can identify weak signals, detect anomalies, and construct more comprehensive understandings of complex scenarios, ultimately fostering a more dynamic and insightful interaction between agents and data.

The convergence of advanced data fabrics and large language models promises a future populated by truly intelligent systems. These systems aren’t simply programmed to react, but are designed to learn from interactions and adapt to changing circumstances, mirroring cognitive flexibility. Crucially, this architecture fosters collaborative potential; agents, powered by efficient data access and reasoning capabilities, can operate not in isolation, but as interconnected entities. This allows for complex problem-solving through distributed cognition, where insights emerge from the interplay of multiple agents in a dynamic environment, ultimately enabling responses to unforeseen challenges and a higher degree of operational resilience.

The pursuit of adaptive data systems, as outlined in the paper, feels less like construction and more like tending a garden. It acknowledges the inherent unpredictability of agentic workloads-systems built to serve reasoning agents aren’t static structures, but evolving ecosystems. This aligns with Donald Davies’ observation that “It is astonishing how little attention is given to the problems of data communication.” The paper’s focus on attention-guided retrieval and semantic caching isn’t about controlling information flow, but about enabling agents to navigate a complex, dynamic landscape. Each deployment, inevitably, becomes a small apocalypse, revealing the limitations of current prophecies and necessitating further adaptation. The true challenge lies not in predicting failure, but in designing for graceful recovery.

The Shifting Landscape

The proposal of an Agent-Centric Data Fabric does not solve the inherent problem, but merely relocates it. Attention-guided retrieval and semantic caching offer optimizations, yet every refinement of access inevitably concentrates dependency. The system may serve agents more efficiently, but it does not diminish the eventual fragility of interconnected reasoning. Increased efficiency, ironically, accelerates the approach of systemic failure; more data moved means more points of potential collapse.

Future work will undoubtedly focus on scaling these techniques, on handling greater volumes of agents and data. However, the more pressing question lies not in capacity, but in resilience. The field must confront the reality that distributed systems are not about preventing failure-failure is guaranteed-but about managing its propagation. Quorum-based serving is a palliative, not a cure.

The pursuit of adaptive data systems, therefore, should not center on building ever-more-complex architectures, but on understanding the dynamics of entropy. The system will not be built; it will grow, and its eventual form will be dictated not by design, but by the inevitable forces of decay. The true metric of success will not be uptime, but the graceful degradation of functionality under stress.

Original article: https://arxiv.org/pdf/2512.09548.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-12-11 22:57