This article explains how to monitor AI agent applications on Amazon Bedrock AgentCore using Grafana Cloud. It covers deployment, observability with OpenTelemetry, and how to debug and optimize performance while tracking costs. A step-by-step tutorial guides you through creating a research assistant agent.

This article explains how to monitor Amazon Bedrock AgentCore AI agents using Grafana Cloud, OpenTelemetry, and Amazon CloudWatch. It covers setting up metric streams to visualize key performance metrics like latency and error rates. You can quickly assess the health and performance of your AI agents in a unified dashboard.

This article outlines how to develop AI agents that enhance productivity and innovation. It emphasizes the importance of quality, governance, and security from the beginning of the development process. The piece also highlights successful examples from companies like Square and Canva.

Designing effective AI agents requires a modular and role-based architecture, deep observability from the start, and robust feedback loops to ensure continuous improvement. Successful implementation of these principles transforms LLMs from static tools into dynamic, autonomous systems capable of adapting to real-world complexities. Understanding the foundational concepts of agent design can bridge the gap between basic AI applications and more sophisticated, self-improving AI agents.