AI agents in enterprises: Best practices with Amazon Bedrock AgentCore

This article provides nine essential best practices for building enterprise-grade AI agents using Amazon Bedrock AgentCore, covering the full development lifecycle from initial planning to organizational scaling.

• Start with clearly defined, narrow use cases rather than attempting broad functionality
• Instrument observability from day one using OpenTelemetry traces and CloudWatch dashboards
• Create detailed tool definitions with clear parameters, return formats, and error handling
• Build automated evaluation frameworks with ground truth datasets and quality metrics
• Decompose complex tasks into specialized multi-agent systems with clear orchestration patterns
• Enforce security through session isolation, personalization, and defense-in-depth access control
• Use deterministic code for calculations and rules; reserve agents for reasoning tasks
• Implement continuous testing with A/B testing, drift detection, and automated rollbacks
• Establish platform teams to maintain tool catalogs, standards, and shared infrastructure across organization

The article emphasizes that production-ready agents require disciplined engineering practices, robust architecture, and continuous improvement—not just connecting models to APIs. AgentCore provides integrated services for runtime, memory, identity, observability, and evaluation to support these practices at scale.