AWS re:Invent 2025 - AI Agents for Databases: Discover, Recommend, Optimize (DEV315)

AI Agents for Databases: Discover, Recommend, Optimize

The Challenge: Reactive Database Management

• Databases often encounter performance issues, storage/compute cost disruptions, and other problems that disrupt workflows
• Developers struggle to diagnose root causes and prioritize fixes amidst a flood of metrics and alerts
• Scaling costs and usage patterns that don't match predictions lead to over-provisioning and cost overruns
• Lack of context and feedback loops between performance and business impact leads to reactive, misaligned decision-making

The Vision: Proactive, Intelligent Database Partners

• Move from reactive, alert-driven firefighting to proactive, predictive database management
• Leverage AI agents to separate signal from noise, diagnose issues, and provide actionable recommendations
• Enable databases to self-optimize, self-tune, and become "intelligent teammates" rather than just services to maintain

Why AI Agents?

• Modern databases and cloud environments generate vast amounts of telemetry that humans struggle to interpret
• AI agents can model normal behavior, adapt to changing baselines, and provide context-rich explanations of issues
• AI-powered monitoring can shift from just collecting data to learning from it and automating optimizations

AI Agents for AWS Data Services

Amazon RDS

• Query Performance: AI agents can analyze queries, execution plans, and indexes to recommend optimizations
• Manual Instance Scaling: AI agents can right-size instances based on CPU, memory, and I/O utilization
• Storage Optimization: AI agents can right-size storage volumes based on usage patterns and growth trends

Amazon Redshift

• Skewed Queries: AI agents can analyze query logs, data distribution, and node utilization to recommend data/workload redistribution
• Inefficient Joins: AI agents can review query plans and schema to suggest denormalization and reduce data shuffling
• Suboptimal Distribution Keys: AI agents can evaluate schema and recommend better distribution strategies

Amazon Aurora

• Replication Lag: AI agents can monitor replication metrics, predict lag, and recommend replica tuning or failover strategies
• Connection Storms: AI agents can detect and manage unused/long-lived connections to prevent resource depletion
• Scaling Bottlenecks: AI agents can forecast workloads and recommend read replica scaling/balancing

Amazon DynamoDB

• Hot Partitions: AI agents can detect uneven data distribution and recommend better partitioning/sharding strategies
• Throttling: AI agents can forecast capacity needs and dynamically adjust provisioned throughput to avoid throttling
• Cost Spikes: AI agents can predict traffic spikes and recommend caching or auto-scaling to control costs

Benefits of AI-Driven Database Management

• Proactive monitoring and issue detection to reduce outages and performance degradation
• Increased DBA productivity by automating analysis, diagnosis, and optimization recommendations
• Intelligent cost management by right-sizing resources and adapting to changing workloads
• Transition from reactive firefighting to proactive, data-driven database engineering

Call to Action

• Start small by deploying AI agents for a single AWS data service and measure the impact
• Integrate AI agents with your organization's runbooks and processes to unlock higher efficiency and resiliency
• Embrace the transition from reactive database systems to proactive, intelligent database partners