AI agents, as complex distributed systems, have lacked proper observability tooling for their intricate operations. Jaeger v2, built on the OpenTelemetry Collector framework, directly addresses this by offering native OTLP ingestion and a unified architecture to trace full agent runs.
This content introduces the "Logic Span" method, which leverages OpenTelemetry to trace and debug hallucinations in Large Language Models (LLMs). By wrapping each "Thought" or "Reasoning Step" in a dedicated OTel Span, developers can identify exactly where an LLM's logic diverges from its intended plan, treating hallucinations like a stack trace.
The content describes implementing SLO-based alerting using OpenTelemetry and Prometheus to address alert fatigue in microservices architectures. It details how these tools standardize telemetry data collection and provide a robust alerting framework, thereby improving system reliability.
This content discusses AI cost attribution, emphasizing the need for request-level identity and pricing data for business-unit chargeback, beyond simple invoices. It suggests a gateway-centric pattern using OpenTelemetry for higher accuracy and outlines a robust financial reconciliation process.
This content explains how to implement OpenTelemetry for tracing Claude API calls in production, addressing issues like slow requests, spiking costs, and poor responses. It highlights why traditional monitoring is insufficient for LLMs and how distributed tracing effectively provides visibility into latency, cost attribution, and soft errors.
This article explores how apcore integrates with OpenTelemetry to transform AI reasoning from a "Black Box" into a transparent, traceable "Glass Box." It introduces the concept of "Thought Span" for debugging non-deterministic AI Agent systems where traditional stack traces are insufficient.
This content addresses the observability gap in LLM-enabled Java services, where standard APM tools fail to track crucial LLM-specific details like prompt usage, token consumption, and costs. It proposes a solution using Spring AI, OpenTelemetry, and self-hosted Langfuse to bridge this gap, offering a fully containerized setup.
This content introduces OpenTelemetry as a solution for debugging complex AI agent failures that traditional logs miss. It highlights how distributed tracing allows developers to visualize the full execution flow of multi-step, asynchronous agent workflows.