The article explains how stacking four distinct loops—Agent, Validation, Event‑driven, and Climbing—using LangChain primitives can turn a fragile LLM‑driven agent into a reliable, self‑improving system without changing the underlying model.
The article explains how to construct reliable LLM-as-a-Judge evaluators by combining deterministic code checks for syntactic validation, designing clear semantic evaluation rubrics, choosing appropriate output formats, calibrating with human‑labeled data, mitigating known model biases, and integrating trace‑based monitoring into production workflows.
LangChain’s Harness Engineering framework tuned system prompts, tool selection, and middleware to turn a rank‑30 programming agent into a top‑5 performer on Terminal Bench 2.0, using trace‑driven analysis, inference‑sandwich scheduling, and context engineering without changing the underlying model.
Trace analysis converts the opaque decision‑making of AI agents into observable data, enabling systematic collection, parallel error detection, targeted improvements, and iterative experimentation, while revealing common failure patterns, budgeting trade‑offs, over‑fitting risks, and cost‑optimization opportunities through a reusable Trace Analyzer Skill framework.
This article analyzes why over 40% of Agentic AI projects fail due to inadequate observability, outlines three common pain points—trace blindness, uncontrolled token costs, and untraceable failures—and demonstrates a practical ADB MySQL solution that reconstructs logs, classifies failures with AI functions, quantifies token waste, and generates prompt‑optimization suggestions, all with a few SQL statements.
This article explains Application Performance Monitoring (APM), its key benefits such as business continuity, performance optimization, and cost reduction, outlines essential APM modules, and details Yunzhou Observation’s OpenTelemetry‑based design, data ingestion, processing, visualization, and future roadmap for observability.
This article presents a systematic root‑cause diagnosis framework for online applications, covering how to identify and resolve both error ("wrong") and performance ("slow") problems using trace links, associated data, high‑quality observability, and large‑language‑model‑driven intelligence.
Xiaohongshu’s AIOps initiative builds a four‑layer framework that leverages machine‑learning‑driven anomaly detection, causal analysis, and trace‑based fault localization to automatically identify root‑cause services in micro‑service environments, achieving over 80 % accuracy across 1000 daily diagnoses while guiding future enhancements in change correlation and automated remediation.
This article explains how to evaluate device overheating using metrics such as CPU/GPU usage and temperature, collect and visualize Trace data with SmartPerf or DevEco Studio, analyze high‑load threads and frequency scaling, and apply targeted code changes—disabling sensors, releasing Lottie resources, and simplifying components—to reduce heat and restore frame‑rate stability.
This guide explains how Alibaba Cloud's ARMS error/slow trace analysis feature can automatically compare abnormal and normal traces to identify root causes such as host, interface, slow SQL, or message‑queue issues, providing step‑by‑step examples for real‑world e‑commerce scenarios.
This article presents ByteDance’s experience building a massive trace‑data analysis platform, covering observability fundamentals, the evolution of its distributed tracing system, various aggregation computation models, technical architecture choices, and concrete use‑cases such as precise topology, traffic estimation, dependency analysis, performance anti‑patterns, bottleneck detection, and error propagation.
This article explains how to build a high‑precision Android startup performance pipeline by selecting the right tracing tool, extending Perfetto with custom Gradle instrumentation, handling edge‑case trace mismatches, and using Trace Processor’s Python API for automated wall‑time and CPU‑time regression detection.
This guide explains how to install and use Perfetto, Android’s next‑generation tracing tool, covering trace capture via command line or Traceur, UI navigation, key trace elements, and practical SQL queries for analyzing slices, counters, scheduling, thread states, and lock contention to optimize performance.
The article examines Oracle’s automatic predicate pushdown that rewrites SQL statements, illustrates its impact on execution plans with trace data and queries, and draws lessons for improving domestic database products and support in the wake of recent geopolitical shifts.
QTracer is Qunar’s internal distributed tracing system that generates a global TraceID for each request, records operations across services, and provides features such as execution chain visualization, log correlation, conditional search, service dependency analysis, database statistics, transparent data propagation, and low‑overhead instrumentation for debugging and performance monitoring.
This article provides a comprehensive technical overview of QTracer, Qunar’s internal distributed tracing platform, covering its architecture, core concepts, key features such as execution‑chain queries, log association, conditional searches, data storage, non‑intrusive instrumentation, bytecode injection, and the QTracer Debug tool for online breakpoint debugging.
This guide demonstrates how to use the ass109.awk script and Oracle's oradebug tool to capture, parse, and analyze trace files, quickly identifying lock and deadlock sessions in Oracle 11gR2 without resorting to costly restarts.
