[Patent 2] How AI Agents Optimize Databases? A GenAI...

The DBA’s Dilemma, The AI Agent’s Opportunity

Database optimization is black magic.

Senior DBAs rely on years of accumulated intuition: looking at a query pattern, they know what index to add; looking at a load curve, they know how to tune the connection pool.

But the problem is: Intuition does not scale, nor is it on call 24/7.

Modern database workloads are too complex for human brains to analyze in real-time. Especially in e-commerce or finance, traffic spikes can explode 10x in seconds. By the time the DBA wakes up to manually fix it, the system is already down.

This was the starting point for my design of M671161 “Smart Optimization System”: Using AI Agents to automate the DBA’s decision-making process.

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Multi-Agent Architecture: Letting AIs “Compete & Cooperate” for the Best Strategy

The most interesting design of this system is its adoption of a multi-AI-sub-agent collaborative competition mechanism.

The overall flow looks like this:

Performance Monitoring Module → Data Preprocessing Module → Load Prediction Module
    → AI Agent Module (Multiple Sub-Agents)
        → Strategy Integration Module
            → Execution Module → Database Server

The AI Agent Module consists of four sub-agents:

Sub-Agent	Area of Responsibility
Query Optimization	Analyzes and rewrites inefficient SQL queries.
Resource Allocation	Dynamic configuration of CPU, Memory, and I/O.
Index Management	Evaluates which indexes to build and which to drop.
Security Assessment	Identifies suspicious query behaviors.

Each of the four sub-agents produces optimization recommendations autonomously. The Strategy Integration Module then consolidates them into the best optimal plan for execution.

Furthermore, the system features a Reinforcement Learning Feedback Mechanism: after executing an optimization, it observes the actual effect, feeding it back to the AI Agent Module for continuous learning.

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The Most Important Insight for GenAI Product Managers

When building AI products, “a single AI solving all problems” is almost impossible.

Truly useful enterprise AI architectures are often collaborative systems of multiple specialized Agents.

This mirrors the microservices concept in software engineering: rather than a monolithic architecture doing everything, it’s better to have multiple services performing their specific duties and coordinating with each other.

As a GenAI Product Owner, the questions you need to ask are:

• Can my problem be broken down into sub-tasks?
• What capabilities do the Agents need for each sub-task?
• How do these Agents collaborate, and how are their outputs integrated?
• How does the entire system learn from the execution results?

Designing Multi-agent architectures is one of the core skills for a GenAI PM post-2025. And this database optimization system is a very concrete implementation example.

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Load Prediction: AI Needs “Future Awareness”

Another critical design in the system is the Load Prediction Module—using deep learning models combined with a historical performance metrics database, time-series data, and calendars (e.g., month-end closings, Double 11 spikes) to predict future database loads.

Preventative optimization is always cheaper than reactive emergency response.

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M671161 Smart Optimization System (AI-Driven Database Performance Prediction and Tuning) | Grant Date: 2025/06/01 | Sole Inventor: Nils Liu

💬 Read more: 2025 Year in Review (English)