Agent-First Data System Architecture

• Agent-first data system architecture is a paradigm where autonomous agents actively manage data workflows through dynamic reasoning and decision-making.
• It utilizes a layered design—comprising agent, coordination kernel, and data resource layers—to enhance scalability, efficiency, and secure integration.
• The framework integrates formal models, optimization, and secure protocols to support real-time data analytics and enterprise-grade applications.

Agent-First Data System Architecture refers to a paradigm in which autonomous agents—often leveraging LLMs or other forms of advanced AI—are positioned as primary actors in data system workflows. Unlike traditional architectures where data manipulation, preprocessing, transfer, analytics, and system management are orchestrated by human operators or static services, the agent-first model introduces one or many software agents that dynamically reason about, transform, and coordinate various data-centric tasks. Architectures in this category span application domains including data preprocessing, integration, large-scale data transfer, data analytics, distributed database failure management, and more. A series of works across the last decade has progressively defined, implemented, and evaluated these architectures.

1. Architectural Foundations and Roles

Agent-first architectures, as exemplified in e-business systems (Kularbphettong et al., 2010), scientific data transfer (Dobre et al., 2011), data analytics (AgenticData (Sun et al., 7 Aug 2025), Data Agent (Sun et al., 2 Jul 2025)), and administrative databases (Zhang et al., 9 Apr 2025), center on autonomous software agents that act as active, decision-making entities rather than passive service endpoints.

Architecture is typically layered and modular:

• Agent Layer: Provides autonomous, distributed, and often role-specialized agents (preprocessing agents, metric agents, orchestration/manager agents).
• Coordination/Kernel Layer: Manages agent discovery, communication, scheduling, and resource sharing. For LLM-based agents, this is often realized as an agent operating system kernel (e.g., AIOS (Mei et al., 25 Mar 2024)).
• Data Resource Layer: Encompasses curated databases, raw data stores, external web APIs, and/or multi-modal enterprise data assets.

Agents implement well-specified protocols, either for communication (e.g., FIPA ACL in JADE-based systems (Kularbphettong et al., 2010, Benmerzoug, 2013)) or for integrating with application logic, data engines, and storage backends.

Agents may be instantiated for:

• Data preprocessing (e.g., missing value imputation as in the e-Wedding project (Kularbphettong et al., 2010))
• Failure detection and remediation (role-aware agents in distributed DBMSes (Zhang et al., 9 Apr 2025))
• Multi-modal data analytics and planning (AgenticData (Sun et al., 7 Aug 2025), Data Agent (Sun et al., 2 Jul 2025))
• System and application-level monitoring and control (LISA for data transfer (Dobre et al., 2011))
• Human-agent collaboration processes (Wang et al., 13 Jun 2025)
• Semantic schema refinement (Rissaki et al., 25 Nov 2024)
• Secure and decentralized multi-agent services (NANDA (Wang et al., 5 Aug 2025))

2. Key Agent Capabilities and Workflow Patterns

Core agent functions in this paradigm include perception (discovery and profiling of data sources), reasoning (decomposing complex tasks into sub-plans or views), planning (constructing and optimizing execution workflows), action (effectuating transformations, transfers, queries, or repairs), and communication (exchanging signals, logging, or coordination information).

Workflow orchestration is achieved by protocols or planners which may:

• Assign roles (system/data/task) and delegate subtasks (AgentFM meta-agent (Zhang et al., 9 Apr 2025))
• Translate high-level requests to agent plans (Planner and Development squads in AutoData (Ma et al., 21 May 2025))
• Route artifacts via structured messages or streams (blueprint architectures (Kandogan et al., 10 Apr 2025), oriented hypergraphs (Ma et al., 21 May 2025))
• Monitor progress and collect feedback for re-planning (as in AgenticData's feedback-driven cycle (Sun et al., 7 Aug 2025))

Planning and validation often involve iterative, feedback-driven loops in which an initial plan is proposed, validated (by either a cross-checking agent or an external mechanism), and corrected until executable. This is explicit in the collaborative schema refinement process, where Analyst, Critic, and Verifier agents drive toward increasing semantic clarity and correctness (Rissaki et al., 25 Nov 2024).

3. Resource Management, Scalability, and System Isolation

Agent-first architectures confront and address challenges of scale and resource contention by introducing dedicated management layers. AIOS (Mei et al., 25 Mar 2024) introduces an explicit kernel layer which centralizes scheduling, memory management (trie-compression, K-LRU eviction), access control, and context management via interrupts and snapshots. In high-throughput agentic speculation (see (Liu et al., 31 Aug 2025)), systems must accommodate thousands of semi-redundant "probe" queries per second, favoring approaches that:

• Support parallel and asynchronous execution
• Utilize agentic memory stores for metadata and partial result caching
• Implement transaction management that is compatible with speculative forking and rollback
• Enable multi-query optimizations, caching, and partial result sharing

For multi-agent deployments spanning multiple infrastructure protocols (MCP/A2A/NLWeb/HTTPS), inter-agent protocol translators and unified discovery/registration systems (as in NANDA (Wang et al., 5 Aug 2025)) are necessary for interoperability at scale.

4. Formal Models, Optimization, and Verification

A consistent feature of recent agent-first architectures is explicit modeling, verification, and optimization of agent behavior:

• Mathematical formulations specify allocation, imputation, or failure detection. For example, donor-based imputation in preprocessing is captured as

$Y_j = \sum_{i \in \text{donors}} d_{ij} w_{ij}$

(Kularbphettong et al., 2010)

• Protocol and workflow verification uses models such as Colored Petri Nets (CPN) for protocol behavior (Benmerzoug, 2013) or first-order logic for safety/liveness in BDI IMS (Akhtar et al., 2015).
• Semantic optimization in agentic data analytics minimizes inference costs subject to quality constraints, e.g.,

$$\text{Cost} = \sum \left[\text{Cardinality} \times (|\text{InputToken}| \cdot \text{Fee}_{in} + |\text{OutputToken}| \cdot \text{Fee}_{out})\right]$$

(Sun et al., 7 Aug 2025)

• Hierarchical and skill-based benchmarking for agent selection and multi-agent task assignment to ensure robustness (Data Agent (Sun et al., 2 Jul 2025)).

Verification and optimization enable agents to assure correctness while maintaining cost and throughput targets.

5. Security, Interoperability, and Governance

Security and governance in agent-first systems require new primitives for trust, visibility, and operational oversight:

• Zero Trust Agentic Access (ZTAA): NANDA establishes provenance and capability verification through W3C Verifiable Credentials, cross-protocol authentication, and least-privilege context sharing (Wang et al., 5 Aug 2025).
• Agent Visibility and Control (AVC): Centralized monitoring, logging, identity, and operational traceability ensure compliance and safeguard against impersonation, spoofing, and supply-chain attacks.
• Regulatory compliance: Both enterprise and consumer agent ecosystems benefit from formal filtering strategies (skills, location, safety certifications), real-time operational controls, and indelible audit logs to counteract misuse and protect sensitive data.

These mechanisms underpin both intra-organization and cross-organization agent interactions in scalable, distributed or decentralized environments.

6. Impact, Practical Applications, and Future Research

Agent-first architectures are validated in practical, production-scale systems:

• Business-driven data models: Human- and agent-centric integration at the business entity level supports rapid time-to-market and semantic alignment (BSDS (Pang, 5 Jun 2025)).
• Autonomous data collection: Multi-agent systems reliably collect, validate, and structure data from open web sources with lower cost and higher performance than wrappers or monolithic LLM approaches (AutoData (Ma et al., 21 May 2025)).
• Enterprise data orchestration: Blueprint architectures using streams and modular registries tie together proprietary models, APIs, and data sources (Compound AI blueprint (Kandogan et al., 10 Apr 2025)).

Current and future research priorities include optimization with adaptive network theory (Pang, 5 Jun 2025), full autonomy for agent-driven data platforms, the design of unified, inspectable process and workflow models (Wang et al., 13 Jun 2025), and the secure, regulated federation of agents at both consumer and enterprise scales (Wang et al., 5 Aug 2025).

Agent-first data system architecture is thus defined by the elevation of agents—autonomous, communicating, and often LLM-augmented—to primary actors in all data-centric system functions, underpinned by modular, scalable, and secure frameworks that support dynamic orchestration, optimization, and business-goal alignment across heterogeneous and evolving datasets and workloads.