AI Observability: Logs, Prompts, Tool Calls, And Cost
Here's a five-line function. It calls an LLM, logs the answer, returns it. async function ask ( question : string ) { const res = await openai . respo…
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The Reason Your Agent Demo Isn't in Production Has Nothing to Do With the Model
Your agent demo took an afternoon. The reason it isn't in production nine months later has nothing to do with the model. I've watched this play out at…
Engineering Design Document: Reusable Observability Platform V2
A production-focused redesign of a Stage 6 LGTM observability platform, moving from a single-service Anvila monitoring setup to a reusable, secure, hi…
LLM Spend Audit: The 45-Minute Diagnostic for Startups
Disclosure: This article may later include affiliate links or service CTAs. Recommendations are based on workflow fit, not commissions. LLM Spend Audi…
Errors, traces, logs, metrics: when to reach for what
When should I reach for a log, a trace, or a metric? I hit that question constantly when I instrument code, and I watch coding agents hit it too. It s…
GPU Incident at 3am: eBPF Tracing from Page to Root Cause in 60 Seconds
TL;DR 3am page: GPU training pipeline missed its SLA. Datadog shows 95% GPU utilization. nvidia-smi agrees. Everything looks green, but the job is 3x …
Harness Base Definition: The Control System Outside the Model
Harness Base Definition: The Control System Outside the Model Previously, we split Agent into several minimal parts: Model: judge the next step Loop: …
Five Fields AI Agent Run Receipts Probably Need
I have been asking agent builders what they want in a run receipt after an AI agent finishes a task. The answers were better than my original schema. …
FSx for ONTAP Audit Logs with Data Residency in your region with Sumo Logic
TL;DR We built a serverless Lambda pipeline that ships FSx for ONTAP audit logs to Sumo Logic's JP (Tokyo) region deployment. For Japanese enterprises…
AI-Powered Root Cause: Correlating File Access with APM via Dynatrace
TL;DR We built a serverless Lambda pipeline that ships FSx for ONTAP audit logs to Dynatrace via the Log Ingest API v2. The real value: Dynatrace's Da…
High-Cardinality File Access Analysis with Honeycomb + OTel
TL;DR We built a serverless pipeline that ships FSx for ONTAP audit logs to Honeycomb, where its high-cardinality query engine turns file access data …
Как дебажить distroless-контейнер в Kubernetes без shell: ephemeral containers на практике
В прошлой статье мы разбирали kubectl describe pod : как читать вывод, в котором Kubernetes уже часто сам написал причину проблемы — в Events, Conditi…
How to Monitor AI Agents in Production
TLDR Monitoring AI agents in production requires distributed tracing: a single user request fans out into 10 or more internal operations, and logs alo…
Chronos vs Toto: Zero-Shot Forecasting Benchmark Results
Introduction Good forecasts help with capacity planning and quieter alerts. But one traffic spike or memory leak can make any forecast useless. The go…
Eval Set Drift: How to Know When Your Golden Set Went Stale
Book: LLM Observability Pocket Guide: Picking the Right Tracing & Evals Tools for Your Team Also by me: Thinking in Go (2-book series) — Complete …
BrontoScope: AI-Powered Error Investigations
Authored by Marco Aquilanti Today we're introducing BrontoScope , one of the Bronto AI Labs initiatives aimed at reducing user toil, increasing team e…
End-to-End Observability for vLLM and TGI: from DCGM to Tokens
Running large language model inference servers in production exposes gaps that neither stock Prometheus dashboards nor the official documentation of v…
Istio 1.30 Deep Dive — Agentgateway, Ambient Multicluster, TrafficExtension API, and 4 CVE Patches (JWKS RSA Leak, XDS Debug Auth)
Istio 1.30 Deep Dive — Agentgateway, Ambient Multicluster, TrafficExtension API, and 4 CVE Patches (JWKS RSA Leak, XDS Debug Auth) On May 18, 2026, th…
The CDN Logging Crisis
Authored by Benoit Gaudin Every second, your CDN is generating thousands of logs that tell a critical story about your application's performance, secu…
Когда метрики сходят с ума: автоматическая детекция аномалий во временных рядах в Yandex Monium
В инфраструктуре Яндекса работают тысячи микросервисов, которые каждую секунду генерируют миллионы временных рядов — метрик. Это могут быть количества…
Logs vs. Metrics: Which is More Effective for Troubleshooting?
When something goes wrong in our systems, the first thing that usually comes to mind is the question "why?" To find the answer, we turn to two main to…
After 5 years of Go services, here's the boilerplate I wish existed
TL;DR: I open-sourced rock288/go-mongo-boilerplate — a Go 1.25 service template that ships the boring production stuff (observability, retry, DLQ, SSR…
Why Your FSx for ONTAP Audit Logs Deserve Better Than EC2
TL;DR FSx for ONTAP file access audit logs are usually consumed through EC2-based patterns — mounted audit volumes and agent-based forwarders such as …
Build Your Own Telemetry UI Using Lovable & Bronto
Authored by Feargal Karney & Mati Remi The Bronto REST API now exposes everything our own UI is built on. That means you can build a custom interf…
Coding agents produce causal DAGs, not logs
Coding agents produce causal DAGs, not logs I've been building tracing hooks for coding agents — Claude Code, Codex CLI, Copilot, and others. The goal…
Девять испытаний роста нагрузки: от стартапа к приложению для 25 миллионов пользователей
Эта статья совсем не технический анализ, а увлекательный рассказ о том, как маленький, но очень перспективный стартап стал топовым приложением, а такж…
What Happens During an Incident (Part 4)
In Part 3, we separated signals on purpose: metrics tell you where to look logs and traces tell you what happened audit tells you what can be proven l…
Turning Observability into a Search Space
In the Mlops world, people have long used DAGs/graphs or at least the consensus has been that best practice was to use them. With AI and agents, the t…
What KubeCon Amsterdam 2026 Taught Me About Infrastructure as Transformation
KubeCon + CloudNativeCon EU 2026 · Amsterdam · March 23–26 More than 13,000 engineers gathering around infrastructure might sound excessive until you …
Beyond Monitoring: Building AI-Powered Predictive Observability for Retail Data Pipelines published
Three numbers before we start: Average detection time with traditional monitoring: 4.2 hours Average detection time with predictive observability: 11 …