Autonomous PostgreSQL: When AI Agents Become Your DBA
Presented by:
Vivek Singh
Vivek Singh is a Principal Database Specialist with AWS focusing on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL engines. He works with enterprise customers providing technical assistance on PostgreSQL operational performance and sharing database best practices. He has over 17 years of experience in open source database solutions, and enjoys working with customers to help design, deploy, and optimize relational database workloads on AWS.
Shaily Porwal
Shaily Porwal is a Senior Technical Account Manager at Amazon Web Services, specializing in Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL. With over 18 years of experience in open-source database solutions, she serves as a trusted advisor to enterprise customers on PostgreSQL operational excellence, performance optimization, and database best practices. As an expert member of the Database Technical Field Community, Shaily mentors Technical Account Managers and leads initiatives including the PostgreSQL Upgrade program. She specializes in complex database migrations, major version upgrades, and architecting scalable PostgreSQL solutions integrated with AI/ML workloads.
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PostgreSQL operations are evolving from reactive monitoring to autonomous, AI-powered management. Drawing from my experience with dozens of customers at AWS, this session demonstrates how agentic AI leveraging MCP servers can automate routine DBA tasks: running comprehensive health checks, tuning database parameters based on workload patterns, and identifying and removing duplicate or unused indexes. We'll explore practical implementations using pgvector with HNSW indexing in agentic AI workloads, intelligent connection pooling, and automated monthly database reporting. Learn how AI agents can detect, diagnose, and resolve common PostgreSQL issues autonomously—performing the essential tasks that regular DBAs handle daily. Discover architectural patterns for integrating agentic AI into your PostgreSQL operations workflow. Key Takeaways:
- Build AI agents using MCP servers to automate PostgreSQL DBA tasks including health checks, parameter tuning, maintenance tasks, and performance optimization
- Use pgvector with HNSW indexing to store historical database patterns as vector embeddings for context-aware AI recommendations
- Implement the Identify, Detect, and Resolve (IDR) framework for autonomous database management that reduces MTTR
- Apply actionable architectural patterns for integrating agentic AI into PostgreSQL operations workflow
- Date:
- Duration:
- 50 min
- Room:
- Conference:
- Postgres Conference: 2026
- Language:
- Track:
- Ops
- Difficulty:
- Medium