Integration roundup: Monitoring your AI stack
Datadog | The Monitor blog

Integration roundup: Monitoring your AI stack


Summary

This article details how to use Datadog's LLM Observability tools to monitor and troubleshoot agents built on Amazon Bedrock. It explains how to track key metrics like token usage, latency, and error rates to understand agent performance and cost, enabling proactive identification and resolution of issues. Ultimately, integrating Datadog provides valuable insights for optimizing Bedrock-powered agents and ensuring a reliable user experience.
Read the Original Article

This article originally appeared on Datadog | The Monitor blog.

Read Full Article on Original Site

Popular from Datadog | The Monitor blog

1
Datadog LLM Observability natively supports OpenTelemetry GenAI Semantic Conventions
2
Introducing Bits AI Dev Agent for Code Security
Introducing Bits AI Dev Agent for Code Security

Datadog | The Monitor blog Mar 26, 2026 85 views

4
Understand session replays faster with AI summaries and smart chapters
Understand session replays faster with AI summaries and smart chapters

Datadog | The Monitor blog Apr 2, 2026 71 views

5
Monitoring MongoDB performance metrics (MMAP)
Monitoring MongoDB performance metrics (MMAP)

Datadog | The Monitor blog May 25, 2016 71 views