Amazon Web Services (AWS) has published a new technical guide showing how developers can integrate Langfuse observability with Amazon Bedrock AgentCore, enhancing visibility into the performance and behavior of advanced AI agents deployed on the AWS platform. This combined solution aims to help teams monitor, debug, and optimize agentic applications that rely on large language models (LLMs) and complex workflows.
As organizations build more intelligent applications, traditional monitoring tools fall short in tracking the intricate reasoning and interactions that AI agents perform. Agent observability fills this gap by exposing detailed traces of agent execution, making it possible to understand how agents make decisions and interact with tools in real time. This transparency is essential for reliability, performance tuning, and cost management.
Amazon Bedrock AgentCore is AWS’s managed platform for deploying and operating scalable, secure AI agents. It provides purpose‑built infrastructure that integrates with open‑source frameworks such as Strands, CrewAI, LangGraph, and LlamaIndex, and supports models both inside and outside of Bedrock. AgentCore emits telemetry data in an OpenTelemetry (OTEL)‑compatible format, enabling seamless integration with monitoring solutions.
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By pairing this telemetry with Langfuse, developers gain hierarchical trace structures that map an agent’s lifecycle, including token usage, latency, tool invocations, errors, and other key metrics. Traces flow through Langfuse’s OTEL backend endpoint, giving teams the ability to diagnose issues, audit agent decisions, and identify performance bottlenecks quickly—capabilities that are especially valuable for complex LLM chains with tools and nested operations.
The blog post includes a step‑by‑step walkthrough showing how to deploy a Strands‑based agent on AgentCore Runtime, export its telemetry via OTEL, and visualize traces in Langfuse. Developers can use this approach to improve operational confidence, support observability best practices, and maintain cost efficiencies as they scale agentic AI in production environments.
This integration underscores AWS’s broader push to make AI agents easier to operate at scale and gives engineering teams powerful tools to understand and optimize their AI workflows.