Guide contents
Monitoring AI Agents: Metrics, Tracing, and Root Causes of Failures
Standard server and application APM fails for agents: an agent can return HTTP 200, burn through tokens, and still give the user an incorrect answer or get stuck in a loop.
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Related Ecosystem Resources
Explore practical workflows, core terminology, and engineering tools
Agent Observability & Tracing (OpenTelemetry)
Methods for collecting metrics, tracing reasoning chains (Spans), analyzing tool latencies, and monitoring token costs using OpenTelemetry and specialized platforms (Langfuse, Arize).
How to find and fix errors in the work of AI agents
We analyze three levels of debugging: from filtering launches to changing the model
Hallucination Triage & Root Cause
A systematic engineering process for analyzing AI model errors: diagnosing context defects, identifying prompt conflicts, and establishing systemic rules instead of mindless query repetition.
AI in Business Processes: From Diagnostics to a Production-Ready Hermes Agent
A comprehensive practical B2B playbook covering process diagnostics, single-agent workflows, tooling, memory, security, evaluation, observability, and runbooks.
Alpine Linux reports OpenSSH password-auth bug that can grant root access
Alpine Linux describes an OpenSSH authentication flaw where any sequence of 257–1024 characters may yield root access.
OpenAI agents found 948 vulnerabilities in the Linux kernel
OpenAI researchers used an unreleased cybermodel and an autonomous pipeline to identify, reproduce, and patch Linux kernel flaws, confirming 948 privilege-escalation vulnerabilities.