Ai Agent Observability With Langfuse

Bonisiwe Shabane

-Jan 27, 2026, 2:41 AM

Easily monitor, trace and debug your AI agents. Explore tools like LangGraph, Llama Agents, Dify, Flowise, and Langflow, and see how Langfuse helps to monitor and optimize your application. An AI agent is a system that autonomously performs tasks by planning its task execution and utilizing available tools. AI Agents leverage large language models (LLMs) to understand and respond to user inputs step-by-step and decide when to call external tools. An AI agent usually consists of 5 parts: A language model with general-purpose capabilities that serves as the main brain or coordinator, and four sub-modules: a planning module to divide the task into smaller... In single-agent setups, one agent is responsible for solving the entire task autonomously.

In multi-agent setups, multiple specialized agents collaborate, each handling different aspects of the task to achieve a common goal more efficiently. These agents are also often referred to as state-based or stateful agents as they route the task through different states. Observing agents means tracking and analyzing the performance, behavior, and interactions of AI agents. This includes real-time monitoring of multiple LLM calls, control flows, decision-making processes, and outputs to ensure agents operate efficiently and accurately. The rise of artificial intelligence (AI) agents marks a change in software development and how applications make decisions and interact with users. While traditional systems follow predictable paths, AI agents engage in complex reasoning that remains hidden from view.

This invisibility creates a challenge for organizations: how can they trust what they can’t see? This is where agent observability enters the picture, offering deep insights into how agentic applications perform, interact, and execute tasks. In this post, we explain how to integrate Langfuse observability with Amazon Bedrock AgentCore to gain deep visibility into an AI agent’s performance, debug issues faster, and optimize costs. We walk through a complete implementation using Strands agents deployed on AgentCore Runtime followed by step-by-step code examples. Amazon Bedrock AgentCore is a comprehensive agentic platform that can deploy and operate highly capable AI agents securely, at scale. It offers purpose-built infrastructure for dynamic agent workloads, powerful tools to enhance agents, and essential controls for real-world deployment.

AgentCore is comprised of fully managed services that can be used together or independently. These services work with any framework including CrewAI, LangGraph, LlamaIndex, and Strands Agents, and any foundation model in or outside of Amazon Bedrock, offering flexibility and reliability. AgentCore emits telemetry data in standardized OpenTelemetry (OTEL)-compatible format, enabling easy integration with an existing monitoring and observability stack. It offers detailed visualizations of each step in the agent workflow, enabling inspection of an agent’s execution path, audit intermediate outputs, and debugging performance bottlenecks and failures. Langfuse uses OpenTelemetry to trace and monitor agents deployed on Amazon Bedrock AgentCore. OpenTelemetry is a Cloud Native Computing Foundation (CNCF) project that provides a set of specifications, APIs, and libraries that define a standard way to collect distributed traces and metrics from an application.

Users can now track performance metrics including token usage, latency, and execution durations across different processing phases. The system creates hierarchical trace structures that capture both streaming and non-streaming responses, with detailed operation attributes and error states. Through the /api/public/otel endpoint, Langfuse functions as an OpenTelemetry Backend, mapping traces to its data model using generative AI conventions. This is particularly valuable for complex large language model (LLM) applications utilizing chains and agents with tools, where nested traces help developers quickly identify and resolve issues. The integration supports systematic debugging, performance monitoring, and audit trail maintenance, making it easier for teams to build and maintain reliable AI applications on Amazon Bedrock AgentCore. Observability tools for AI agents, such as Langfuse and Arize, help gather detailed traces (a record of a program or transaction’s execution) and provide dashboards to track metrics in real time.

Many agent frameworks, like LangChain, use the OpenTelemetry standard to share metadata with observability tools. On top of that, many observability tools provide custom instrumentation for greater flexibility. We tested 15 observability platforms for LLM applications and AI agents. Each platform was implemented hands-on through setting up workflows, configuring integrations, and running test scenarios. We benchmarked 4 observability tools to measure whether they introduce overhead in production pipelines. We also demonstrated a LangChain observability tutorial using Langfuse.

We integrated each observability platform into our multi-agent travel planning system and ran 100 identical queries to measure their performance overhead compared to a baseline without instrumentation. Read our benchmark methodology. LangSmith demonstrated exceptional efficiency with virtually no measurable overhead, making it ideal for performance-critical production environments. Laminar introduced minimal overhead at 5%, making it highly suitable for production environments where performance is critical. In the high-stakes arena of autonomous AI systems, where agents juggle complex decisions across multi-step workflows, a new breed of monitoring platforms has emerged as indispensable guardians. Tools like Langfuse and AgentOps.ai are transforming opaque agent behaviors into actionable insights, enabling enterprises to deploy reliable, cost-efficient agents at scale.

As AI agents proliferate in production environments—from financial trading bots to customer service orchestrators—these observability platforms address the core challenge: making the invisible visible without crippling performance. Observability for AI agents goes beyond traditional logging. It captures granular traces of prompts, tool calls, reasoning chains, and outputs, providing dashboards for real-time metrics on latency, costs, and errors. "Observability tools for AI agents, such as Langfuse and Arize, help gather detailed traces and provide dashboards to track metrics in real time," notes a comprehensive benchmark from AIMultiple Research, updated January 22, 2026. This necessity arises from agents’ unpredictable nature: a single hallucination or faulty tool invocation can cascade into costly failures. Challenges abound in agent monitoring.

Multi-agent interactions amplify events, while deep instrumentation adds latency. AIMultiple’s hands-on benchmarks tested five platforms on a multi-agent travel booking system, measuring overhead as the percentage increase in latency. LangSmith led with 0% overhead, followed by Laminar at 5%, AgentOps at 12%, and Langfuse at 15%. "AgentOps and Langfuse showed moderate overhead at 12% and 15% respectively, representing a reasonable trade-off between observability features and performance impact," the report states. Langfuse’s Open-Source Edge in Prompt Mastery Langfuse, an open-source LLM engineering platform, excels in end-to-end tracing for prompts, responses, and multi-modal inputs like text, images, and audio.

Features include sessions for user-specific tracking, environments for dev/prod separation, agent graphs for workflow visualization, and token/cost monitoring with masking for privacy. Free up to 100,000 observations monthly, it starts at $29 for unlimited users. "Langfuse offers deep visibility into the prompt layer, capturing prompts, responses, costs, and execution traces for debugging, monitoring, and optimizing LLM applications," per AIMultiple. 📚 AI Agent Evaluation Series - Part 4 of 5 Building AI agents is exciting. Debugging them when they fail in production?

Not so much. Here's the problem: AI agents don't fail like traditional software. There's no stack trace pointing to line 47. Instead, you get vague responses, hallucinations, or worse—confidently incorrect answers. Your users see the failure, but you have no idea why the agent decided to call the wrong tool, ignore context, or make up facts. The solution?

Observability and evaluation systems built specifically for AI. In this guide, we'll show you how to use Langfuse to debug AI agents effectively. You'll learn how to trace agent execution, analyze LLM calls, build evaluation datasets, and implement automated checks that catch issues before your users do. Whether you're running simple RAG pipelines or complex multi-agent systems, these techniques will help you ship reliable AI applications. This repository contains AI Agents integrated with observability tools to track usage, inputs, and outputs. The integration allows monitoring of each Agent's execution and logging important events.

In this cookbook, we will learn how to monitor the internal steps (traces) of the OpenAI agent SDK and evaluate its performance using Langfuse. This guide covers online and offline evaluation metrics used by teams to bring agents to production fast and reliably. To learn more about evaluation strategies, check out this blog post. Below we install the openai-agents library (the OpenAI Agents SDK), the pydantic-ai[logfire] OpenTelemetry instrumentation, langfuse and the Hugging Face datasets library In this notebook, we will use Langfuse to trace, debug and evaluate our agent. Note: If you are using LlamaIndex or LangGraph, you can find documentation on instrumenting them here and here.

Posted on Aug 18, 2025 • Edited on Jan 22 • Originally published at builder.aws.com 🇻🇪🇨🇱 Dev.to Linkedin GitHub Twitter Instagram Youtube Linktr Getting Started with Strands Agents: Build Your First AI Agent - FREE course This third part of the Building Strands Agents series focuses on implementing observability with LangFuse to monitor your agents in real-time. When you deploy agents in production, you need to answer these questions: Does your agent respond accurately? How long do responses take?

Where are the bottlenecks? Which conversations fail, and why? Building a new application powered by Large Language Models (LLMs) is an exciting venture. With frameworks and APIs at our fingertips, creating a proof-of-concept can take mere hours. But transitioning from a clever prototype to production-ready software unveils a new set of challenges, central among them being a principle that underpins all robust software engineering: observability. If you've just shipped a new AI feature, how do you know what's really happening inside it?

How many tokens is it consuming per query? What's your projected bill from your language model provider? Which requests are failing, and why? What data can you capture to fine-tune a model later for better performance and lower cost? These aren't just operational questions; they are fundamental to building reliable, scalable, and cost-effective AI applications. Observability is the key to answering these questions.

It is especially critical in the world of LLMs, where the non-deterministic nature of model outputs can introduce a layer of unpredictability that traditional software doesn't have. Without observability, you're usually flying blind. Fortunately, instrumenting your application for observability is no longer the difficult task it once was. The modern AI stack has matured, and integrating powerful observability tools can be surprisingly straightforward. Let's explore how to do this with Langflow to see these concepts in action. At its core, observability in an AI context involves capturing data at each step of your application's logic.

Ai Agent Observability With Langfuse

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