AI Factory

Agent Requirements Document (ARD) for

Schema Optimization Agent

An intelligent AI agent that analyzes transformed data warehouse schemas and provides actionable optimization recommendations to enhance performance, reduce costs, and improve data quality.

Goal: To autonomously analyze data warehouse schemas post-transformation and suggest structural improvements that optimize query performance, storage efficiency, and data integrity while maintaining business logic compliance.

Core Intelligence Layer Requirements

The agent's analytical "brain," defining its ability to understand schema patterns, reason about optimization opportunities, and strategically plan database improvements.

Strategy Layer

  • Schema Analysis Planning: Decompose complex database structures into analyzable components (tables → columns → relationships → constraints).
  • Optimization Goal Mapping: Align schema improvements with business objectives like "reduce query latency by 40%" or "optimize storage costs."
  • Priority Scoring: Rank optimization opportunities based on impact, complexity, and business criticality.
  • Rollback Strategy: Plan safe implementation paths with automated rollback capabilities for each optimization.

Memory Layer

  • Schema Pattern Recognition: Store and recall successful optimization patterns from previous schema transformations.
  • Performance Baseline Storage: Maintain historical performance metrics and query patterns for trend analysis.
  • Business Rule Memory: Remember critical business constraints and compliance requirements that must be preserved.
  • Optimization History: Track past optimization attempts, their outcomes, and lessons learned for future improvements.

Reasoning Layer

  • Multi-dimensional Analysis: Execute complex analysis considering performance, storage, maintenance, and scalability impacts simultaneously.
  • Chain of Reasoning (CoR): Provide detailed explanations for each optimization recommendation with supporting evidence and trade-offs.
  • Constraint Validation: Ensure all proposed changes respect foreign key relationships, data integrity, and business rules.
  • Risk Assessment: Calculate confidence scores and potential risks for each optimization recommendation.

Adapters Layer Requirements

Specialized interfaces that enable the agent to analyze database schemas, execute optimizations, and learn from performance outcomes in real-world environments.

Perception

  • Schema Introspection: Analyze table structures, column types, indexes, constraints, and relationships across multiple database systems.
  • Query Pattern Analysis: Process query logs to understand access patterns, join frequencies, and performance bottlenecks.
  • Metadata Extraction: Read database catalogs, statistics, and configuration settings to understand current state and capabilities.

Tool Execution

  • Database Operations: Execute schema modifications, index creation, and constraint updates across various database platforms (PostgreSQL, MySQL, Snowflake).
  • Performance Testing: Run automated benchmarks and query performance tests to validate optimization impacts.
  • Schema Migration: Generate and execute safe migration scripts with validation checkpoints and rollback capabilities.
  • Monitoring Integration: Connect with database monitoring tools to track performance metrics and optimization outcomes.

Learning

  • Outcome Analysis: Learn from the success or failure of applied optimizations to improve future recommendations.
  • Pattern Recognition: Identify recurring schema anti-patterns and successful optimization strategies across different workloads.
  • Adaptive Tuning: Continuously refine optimization algorithms based on observed performance improvements and business impact.

Interaction

  • Developer Dashboard: Provide intuitive visualization of schema health, optimization recommendations, and implementation roadmaps.
  • Approval Workflows: Route high-impact changes through appropriate stakeholders with detailed impact assessments.
  • Real-time Notifications: Alert DBAs and developers about critical schema issues or successful optimization completions.

Deployment

  • Multi-Database Support: Deploy across heterogeneous database environments with unified optimization strategies.
  • Containerized Execution: Run as lightweight containers that can be deployed close to target databases for minimal latency.
  • Scalable Architecture: Handle multiple database instances simultaneously with configurable resource allocation.

Observability

  • Optimization Tracking: Monitor the lifecycle and impact of every schema optimization from recommendation to implementation.
  • Performance Metrics: Track query performance improvements, storage savings, and maintenance time reductions.
  • Audit Trail: Maintain comprehensive logs of all schema changes with rollback information and approval chains.

Cross-Cutting Concerns Layer Requirements

Foundational principles ensuring the agent operates safely, compliantly, and delivers measurable business value while maintaining data integrity and system reliability.

Security

  • Data Protection: All schema analysis and optimization operations respect data privacy and security classifications.
  • Access Control: Implement role-based permissions ensuring only authorized personnel can approve schema changes.
  • Secure Communications: All database connections use encrypted channels with certificate validation and credential rotation.

Ethics

  • Data Integrity: Never compromise data accuracy or business logic compliance in pursuit of performance optimization.
  • Transparency: Provide clear explanations for all optimization recommendations with supporting analysis and potential risks.
  • Bias Prevention: Ensure optimization recommendations don't favor specific applications or user groups unfairly.

Business Value

  • ROI Measurement: Track cost savings from storage optimization, reduced compute resources, and improved query performance.
  • SLA Compliance: Ensure optimizations contribute to meeting or exceeding database performance SLAs and availability targets.
  • Strategic Alignment: Focus optimization efforts on schemas supporting critical business processes and high-value applications.

Compliance

  • Regulatory Adherence: Maintain compliance with data governance regulations (GDPR, SOX, HIPAA) throughout optimization processes.
  • Change Management: Follow enterprise change management procedures with proper documentation and approval workflows.
  • Audit Support: Provide comprehensive audit trails for all schema modifications with business justification and approval evidence.

User Trust

  • Explainable Decisions: Clearly explain why specific optimizations are recommended and how they benefit system performance.
  • Predictable Behavior: Maintain consistent optimization criteria and methodology across different database environments.
  • Human Oversight: Provide clear mechanisms for DBAs to review, modify, or reject optimization recommendations.