Monitoring Agents in Amazon Bedrock with Datadog LLM Observability

The evolution of generative artificial intelligence is revolutionizing the technological landscape, thanks to advancements in large-scale language models and natural language processing. In this context, a new generation of AI systems known as “agents” has emerged, possessing the ability to think, iterate, and execute actions to solve complex tasks, surpassing the limitations of traditional AI assistants.

These autonomous systems not only increase productivity in workplace environments and optimize business workflows but also introduce new operational demands regarding their oversight. The need to ensure adequate performance and reliability with the responsible use of artificial intelligence has led to the development of observability solutions. These tools are essential for monitoring agent behavior and managing their tasks.

Amazon Bedrock has become a key platform in creating these AI systems. It offers developers the ability to build agents quickly and efficiently, enabling the breakdown of complex requests into manageable steps, access to knowledge bases, and the capability to maintain contextual conversations without having to manage the underlying logic of each interaction.

To properly scale these systems, organizations must implement robust observability solutions. Datadog stands out in this area by providing tools that allow teams to operate applications with large language models confidently while monitoring their performance, quality, and security aspects. The integration of Datadog with Amazon Bedrock offers detailed tracking of AI application behavior, facilitating quick identification and resolution of issues.

This collaboration provides a comprehensive view of the application status and allows for tracking every stage of agent execution within complex workflows. Since interactions between agents can influence latency and costs, having clear visibility over resource usage becomes essential.

Thanks to Datadog’s observability, teams can monitor various aspects of performance, from latency at each execution step to the quality and safety of outputs generated by the models. Additionally, developers have the capability to conduct custom evaluations to ensure that results meet expected formats and are free from harmful content.

For those looking to implement generative artificial intelligence tools, this integration offers essential observability capabilities, ensuring that agent AI systems operate optimally and generate real value for organizations. Datadog strengthens its collaboration with AWS, establishing itself as a leader in cloud service integration and providing businesses with the tools necessary to enhance their efficiency in a constantly evolving digital environment.