Autonomous DBOps: Agentic AI for Maintaining SQL Server Databases

Imagine, It's 2 AM on a Tuesday. Your database cluster, the beating heart of your e-commerce platform is suddenly choking. CPU is pegged at 95%, queries are timing out, and you're on-call to fix the issue.

Here's the challenge: you have exactly 30 minutes to identify the culprits, tune the database, and restore stability before leadership wakes up demanding answers.

Sound familiar? We've all been there—manually sifting through CloudWatch metrics, tweaking parameters in panic mode, and hoping our interventions don't make things worse. But what if you didn't have to? What if you had an autonomous Database Agent, a reasoning sidekick, that could diagnose bottlenecks, hypothesize fixes, and implement them autonomously, all while explaining its logic like a senior DBA?

In this session, we're diving headfirst into that world. You'll see a running Amazon RDS for SQL Server database experiencing real performance issues. Learn how to restore the database to healthy performance by building a self-healing system with AI agents powered by the AWS Strands.

Health Check Agent — Identifies top queries by execution time, retrieves execution plans, detects index fragmentation, identifies missing and unused indexes, and generates actionable recommendations

Actions Agent — Implements database optimizations including creating indexes, updating statistics, rebuilding fragmented indexes, and optimizing query plans

Supervisor Agent — Coordinates both agents to form an autonomous system that identifies issues, proposes fixes, and applies them with minimal production impact

CloudWatch Integration — Deploy the Health Check Agent to Amazon Bedrock AgentCore runtime. When triggered by CloudWatch alarms (e.g., CPU exceeding 80%), the agent automatically diagnoses issues and emails recommendations to operators

AWS Services Used:

Amazon RDS for SQL Server
Amazon CloudWatch Database Insights
Amazon Bedrock AgentCore
AWS Strands SDK

Demonstrate how autonomous AI agents can diagnose and resolve SQL Server database performance issues, showcasing the potential of agentic AI for database operations. Key Takeaways for Attendees: Understand Agentic AI for Database Operations — Learn how AI agents powered by AWS Strands can reason, plan, and execute database maintenance tasks autonomously See Real-World Problem Solving in Action — Watch live demonstrations of agents diagnosing performance bottlenecks in a running RDS SQL Server database and implementing fixes Explore Multi-Agent Orchestration — Understand how Health Check, Actions, and Supervisor agents work together to form an intelligent, self-healing database system Learn Integration Patterns — Discover how to integrate agents with CloudWatch alarms for proactive monitoring and automated incident response Evaluate Applicability — Assess how autonomous database agents could reduce MTTR, minimize manual interventions, and improve operational efficiency in your own environment Session Outcome: Attendees will leave with a clear understanding of how agentic AI can transform reactive database operations into proactive automation, along with practical insights on implementation approaches using AWS services.