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Agentic Data Products: Research Initiative

Executive Summary

This research initiative, launched in January 2025, explores the integration of small language models as "agentic sidecars" for data products within our data mesh architecture. By leveraging efficient models and the Multi-agent Cooperation Protocol (MCP), we aim to create autonomous data products that can self-manage, collaborate, and evolve with minimal human intervention while maintaining governance standards.

Background and Motivation

Traditional data products require significant human intervention for maintenance, evolution, and cross-domain coordination. This creates operational overhead and slows responsiveness to changing business needs. Recent advances in smaller, more efficient language models create an opportunity to embed AI capabilities directly within data products, enabling a new class of "agentic data products" that can operate autonomously while coordinating through standardized protocols.

Research Objectives

  1. Evaluate the feasibility of small language models (<1B parameters) as autonomous agents for data products
  2. Design and implement a sidecar architecture for agent integration with minimal resource overhead
  3. Adapt the Multi-agent Cooperation Protocol (MCP) for data mesh environments
  4. Develop and validate autonomous capabilities for data product management
  5. Create governance frameworks for autonomous data product operation

Small Language Model Evaluation

Model Selection Criteria

Criterion Description Target Metrics
Size Model parameter count and memory footprint <1B parameters, <2GB memory
Inference Speed Time to generate responses <100ms for standard operations
Domain Adaptation Ability to fine-tune for specific domains Effective fine-tuning with <1000 examples
Deployment Efficiency Resource requirements in production Compatible with standard Kubernetes pods
Security Vulnerability to prompt injection, data leakage Zero critical vulnerabilities

Candidate Models

  1. MLX-MistralFineTuned: Optimized model running on MLX for Apple Silicon

    • 500M-1B parameters
    • Domain-specific fine-tuning for schema understanding
    • Compatible with MCP for agent coordination

  2. Gemma-2B: Google's lightweight efficiency-focused model

    • 2B parameters (base model)
    • Potential for quantization to further reduce size
    • Strong understanding of data structures

  3. Phi-2: Microsoft's small but capable model

    • 2.7B parameters
    • Excellent performance on reasoning tasks
    • Potential for domain specialization

  4. Custom Distilled Model: Purpose-built for data product operations

    • Teacher-student distillation from larger models
    • <500M parameters
    • Specialized for data operations

Sidecar Architecture

Design Principles

  1. Loose Coupling: The agent sidecar should interact with the data product through well-defined APIs, allowing independent evolution
  2. Resource Efficiency: Minimal CPU/memory footprint to avoid impacting data product performance
  3. Security First: All communication channels secured and authenticated
  4. Observability: Comprehensive logging and monitoring of agent actions
  5. Human Oversight: Critical operations require human approval

Architecture Diagram

┌───────────────────────────────────────┐     ┌───────────────────────────────────┐
│           Data Product Pod            │     │        Agent Sidecar Pod          │
│                                       │     │                                    │
│ ┌─────────────┐     ┌───────────────┐ │     │ ┌────────────┐   ┌──────────────┐ │
│ │             │     │               │ │     │ │            │   │              │ │
│ │ DuckDB      │     │ API Service   │◄┼─────┼─┤ Agent API  │   │ Small LLM    │ │
│ │ Database    │     │ (FastAPI)     │ │     │ │ Client     │   │ Engine       │ │
│ │             │     │               │ │     │ │            │   │              │ │
│ └─────────────┘     └───────┬───────┘ │     │ └────────────┘   └──────────────┘ │
│                             │         │     │        │                 ▲         │
│                             ▼         │     │        ▼                 │         │
│                     ┌───────────────┐ │     │ ┌────────────┐   ┌──────────────┐ │
│                     │               │ │     │ │            │   │              │ │
│                     │ Agent         │◄┼─────┼─┤ MCP        │   │ Knowledge    │ │
│                     │ Interface     │ │     │ │ Protocol   │   │ Store        │ │
│                     │               │ │     │ │ Handler    │   │              │ │
│                     └───────────────┘ │     │ └────────────┘   └──────────────┘ │
│                                       │     │                                    │
└───────────────────────────────────────┘     └───────────────────────────────────┘

Component Description

  1. Agent API Client: Interfaces with the data product, making API calls based on agent decisions
  2. Small LLM Engine: Efficient language model for reasoning and decision making
  3. MCP Protocol Handler: Implements the Multi-agent Cooperation Protocol for coordination
  4. Knowledge Store: Persistent storage of domain knowledge, past decisions, and patterns
  5. Agent Interface: Secure API endpoint for the sidecar to interact with the data product

Multi-agent Cooperation Protocol (MCP) for Data Mesh

Protocol Adaptation

The Multi-agent Cooperation Protocol provides a standardized way for autonomous agents to coordinate, but requires adaptation for the data mesh context:

  1. Domain-Specific Messaging: Extensions for data operations (schema updates, quality checks)
  2. Governance Enforcement: Built-in governance checks and compliance verification
  3. Data Contract Validation: Mechanisms to verify contract adherence between products
  4. Cross-Domain Operations: Protocols for coordinating operations across domain boundaries

Message Types

Message Type Purpose Example Payload
SchemaProposal Propose schema changes {"schema_update": {"add_column": {"name": "risk_factor", "type": "DECIMAL(5,2)"}}}
QualityAlert Report data quality issues {"quality_issue": {"column": "expected_tri", "issue": "null_values", "frequency": 0.15}}
QueryOptimization Suggest query optimizations {"optimization": {"query_pattern": "SELECT * FROM projects WHERE status = ?", "suggestion": "ADD INDEX ON status"}}
ResourceScale Coordinate resource scaling {"scale_event": {"resource": "memory", "current": 512, "suggested": 1024, "reason": "large_query_trend"}}
CoordinationRequest Request cross-domain operation {"operation": "join_datasets", "source": "financing", "target": "risk_assessment", "join_key": "project_id"}

Coordination Workflow

  1. Discovery: Agents discover other data products through a registry
  2. Capability Exchange: Agents share their capabilities and domain expertise
  3. Task Delegation: Work is assigned based on domain ownership and capabilities
  4. Consensus Building: Critical decisions require consensus from affected agents
  5. Execution: Coordinated execution with rollback capabilities
  6. Reporting: Results and outcomes shared with all participants

Autonomous Capabilities

Schema Management

  • Monitoring query patterns to identify optimization opportunities
  • Suggesting new indexes or column additions based on usage
  • Detecting unused schemas or redundant structures
  • Validating schema changes against data contracts

Data Quality

  • Continuously monitoring for data quality issues (missing values, outliers)
  • Suggesting remediation steps for identified issues
  • Implementing automated data cleansing where appropriate
  • Generating quality reports with trend analysis

Query Optimization

  • Analyzing query performance and identifying bottlenecks
  • Suggesting optimization strategies (indexes, materialized views)
  • Implementing approved optimizations
  • Validating performance improvements

Resource Management

  • Monitoring resource utilization patterns
  • Predicting future resource needs based on usage trends
  • Suggesting resource allocation changes
  • Implementing approved scaling operations

Documentation

  • Automatically generating and updating API documentation
  • Creating data dictionaries and glossaries
  • Maintaining usage examples and common patterns
  • Documenting data lineage and transformations

Proof of Concept Implementation

Phase 1: Single-Agent Capability

The initial proof of concept will focus on a single autonomous capability with the following components:

  1. MLX-MistralFineTuned Model Deployment

    • Apple Silicon optimized for development environments
    • Containerized for production deployment
    • Fine-tuned for schema understanding and optimization

  2. Basic Agent Interface

    • REST API for agent-data product communication
    • Authentication and authorization mechanisms
    • Logging and audit trail

  3. Schema Optimization Capability

    • Query pattern analysis
    • Schema improvement suggestions
    • Human approval workflow

  4. MCP Foundation

    • Basic message structure implementation
    • Communication patterns
    • Extensibility for future capabilities

Success Metrics

  • Performance: Agent operations add <5% overhead to data product
  • Accuracy: >90% of schema suggestions are approved by human experts
  • Efficiency: Reduce manual schema optimization time by 50%
  • Adoption: Positive feedback from data product teams on usability

Research Challenges

  1. Model Efficiency: Balancing model capabilities with resource constraints
  2. Security Concerns: Preventing unauthorized actions or vulnerabilities
  3. Governance Integration: Aligning autonomous operations with organizational governance
  4. Trust Building: Creating transparency and trust in agent decision-making
  5. Coordination Complexity: Managing multi-agent interactions at scale

Ethical Considerations

  1. Transparency: All agent actions must be transparent and explainable
  2. Human Oversight: Critical operations require human approval
  3. Privacy Protection: Agents must respect data privacy boundaries
  4. Responsibility: Clear accountability for autonomous actions
  5. Bias Mitigation: Regular evaluation for potential biases in agent decisions

Implementation Timeline

Phase Status Timeframe Key Deliverables
Research & Design ✅ Completed Jan-Feb 2025 Model evaluation report, architecture design
Proof of Concept ✅ Completed March 2025 Single-agent prototype with basic capabilities
Limited Pilot 🔄 In Progress April-May 2025 Deployment with selected data product teams
Multi-agent Prototype ⏳ Planned June-August 2025 Coordinated operations across 3+ data products
Production Planning ⏳ Planned September-October 2025 Governance framework, scaling strategy
Initial Deployment ⏳ Planned November 2025 First production-ready autonomous data products

Current Status (April 2025)

The agentic data products initiative has been active for approximately three months, with significant progress in the initial phases:

  1. Research & Design: We completed the evaluation of small model options and selected MLX-MistralFineTuned (700M parameters) as our base model. Architecture design documents were finalized in February 2025.

  2. Proof of Concept: A single-agent prototype was developed and demonstrated in March 2025, focusing on schema optimization capabilities. The prototype showed a 47% reduction in manual schema optimization time.

  3. Limited Pilot: We are currently beginning the limited pilot phase, working with three selected data product teams. Initial setup has been completed, and we are gathering early feedback on:

    • Schema suggestion quality and relevance
    • Performance impact on existing data products
    • User experience with approval workflows

Next Steps

  1. Short-term (Q2 2025):

    • Complete the limited pilot with detailed performance metrics
    • Begin implementing cross-agent communication patterns
    • Develop initial multi-agent coordination protocols
    • Improve the human-in-the-loop interface based on pilot feedback

  2. Medium-term (Q3 2025):

    • Implement the multi-agent prototype with three coordinated data products
    • Test resilience and recovery mechanisms
    • Refine governance frameworks for autonomous operations
    • Prepare integration plan for the v2.0.0 release

  3. Long-term (Q4 2025 - 2026):

    • Integrate with the November 2025 v2.0.0 release
    • Expand capabilities to include data quality monitoring and self-optimization
    • Scale to 10+ data products in the mesh
    • Contribute to the data product marketplace initiative

Alignment with Product Roadmap

This research initiative directly supports several key features planned for upcoming releases:

  1. Version 1.3.0 (June 2025): Research findings on performance optimization will inform the implementation of query caching and async processing.

  2. Version 2.0.0 (November 2025): Initial agentic features will be integrated, including schema optimization and query pattern detection.

  3. Version 2.1.0 (Q1 2026): Enhanced autonomous capabilities will build upon the multi-agent prototype findings.

  4. Version 3.0.0 (Q3 2026): Fully autonomous data products will represent the culmination of this research initiative.

Conclusion

The Agentic Data Products initiative represents a significant evolution in our data mesh architecture, potentially reducing operational overhead while improving adaptability and coordination. By leveraging small, efficient language models in a sidecar architecture with the Multi-agent Cooperation Protocol, we can create autonomous yet governable data products that enhance the overall data mesh ecosystem.

This research aligns with our broader strategic goals of operational efficiency, rapid adaptation to business needs, and reducing technical complexity for domain teams. The incremental approach allows us to validate benefits and address challenges while building towards a comprehensive autonomous data product framework.

References

  1. MLX: Efficient ML for Apple Silicon - MLX Framework
  2. Multi-agent Cooperation Protocol - Protocol Specification
  3. Data Mesh Architecture Principles - Data Mesh Principles
  4. Small Language Models: Capabilities and Limitations - SLM Research
  5. Autonomous Systems Governance - Governance Framework