How to identify which customers are affected by API failures
Most API monitoring starts with endpoints: Which route is failing? What is the error rate? Did latency increase after a release? Those questions are e…
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Audit, Observability & Lineage for Enterprise AI Agents
The Observability Black Box As autonomous AI agents evolve from isolated chat assistants into multi-agent systems executing multi-step business logic …
Your RAG Index Might Be Lying to You: Data Freshness Is the Missing Signal for AI Systems
A follow-up to How Old Is My Data? The failure mode that gets worse when a machine is reading the data In a classic dashboard, stale data is a human p…
Frontend Observability for Startups: Choosing Tools That Actually Save You Time
How modern frontend teams can use observability tools like Sentry, SonarQube, and LogRocket to debug faster, write cleaner code, and stop bugs before …
The Payment Succeeded. The Governance Failed: Observing AI Agent Boundary Violations with OpenTelemetry and SigNoz
AI agents are increasingly being trusted with actions that affect money, permissions, customer data, and operational systems. But there is a dangerous…
We made SigNoz's LLM observability actually reachable…
-and added the one signal it was missing Track 01 — AI & Agent Observability. A hackathon project built from scratch, July 20–26, 2026. There's a …
Our incident-response agent got the root cause wrong 7 times out of 12. It still never made a bad rollback.
We ran Agent K against 12 seeded production incidents. It named the correct root cause 5 times out of 12 — 3 out of 9 if you discard the runs we could…
We Asked SigNoz How to Flag a Hallucinating Agent. They Said "We Haven't Figured That Out." So We Did.
By Team ThunderBoltz · Agents of SigNoz Hackathon · Track 1: AI & Agent Observability In the kickoff Q&A for the Agents of SigNoz hackathon, w…
We instrumented an AI agent swarm with SigNoz, and its own telemetry told us we were wrong about almost everything
Built for the WeMakeDevs Agents of SigNoz hackathon, July 2026. Mission Control. The graph is the swarm, the river underneath it is the live span stre…
An append-only audit log caught two accounting bugs in a 216-star usage tracker
Last month I wrote about auditing 34 days of multi-model Claude Code usage and finding that a single missing model line was half my overspend. The cor…
trelix v2.7 to v2.9: The Release Where the Pipeline Itself Became the Product
On 2026-07-09 I shipped trelix v2.7.0. The architecture felt done — seven retrieval legs, a knowledge graph, an agentic loop. Then I opened the GitHub…
Mackerel's Log Feature Just Opened in Beta — Here's What It Takes to Wire It Into an OTLP Pipeline
TL;DR Mackerel — Hatena's Japan-origin observability platform — opened its log feature as public beta on July 16, 2026 . In response, this repository …
Fill a WAL Volume on Purpose Before ENOSPC Turns Your Health Check Green
A service reports healthy because its process is alive, while WAL writes fail with ENOSPC . Log rotation also fails, retries amplify load, and deletin…
You don't need an observability stack yet
The question gets asked on r/node every few months, on r/selfhosted every few weeks, and on Hacker News whenever a Datadog invoice goes viral. Some va…
Instrument First, Then Prompt: Finding Real Agentic Pipeline Bugs
The default reaction when an agentic pipeline misbehaves is to open the system prompt and start rewriting it. The instinct makes sense — the prompt is…
Instrumenting an AI-Powered GitHub Analyzer with OpenTelemetry and SigNoz
This article is my submission for the Agents of SigNoz Hackathon : Blog Track, where participants instrument real applications with OpenTelemetry and …
Рентген для нейросетей, или как я перестал понимать собственный ИИ и написал свой APM
Бывало у вас такое: месяцами пилишь архитектуру, фичи летят одна за другой, тесты зелёные. Всё работает. А потом в какой-то момент ловишь себя на мысл…
Exit 0 Is Not Success: Automation Assurance That Verifies Outcomes
A cron that exits zero and produces nothing is not healthy. It is a silent failure wearing a green badge. That distinction drove most of 2026-07-14 on…
The most expensive outages return HTTP 200
A 500 is an unexpected jolt from your sleep, while a 200 silently drains your savings. That's the bug you never get trained for. All is functioning. E…
Why a Coding-Agent Completion Event Is Not Enough
A terminal monitor sees task_complete . It sends an alert. The user returns and finds that the event belongs to an old turn, a background worker, or a…
How to Review a Signup Regression After a Next.js Release
A signup regression after a Next.js release should not start with panic. It should start with a narrow review. The dangerous version is familiar: a de…
Debug a Legacy Frontend-Backend Deployment With One Trace ID
Legacy frontend-backend deployments fail in layers. A React build can be correct while the proxy serves last week's files. Django can be healthy while…
5 Ways Your AI Agent Will Fail (And How to Prevent Them)
Your agent works in testing. Then you deploy it and things break in ways you didn't expect. Here are five failure modes I've seen repeatedly, with Typ…
When an LLM answer is wrong, the trace is where you look. Some tools make that easy.
A user reports a hallucinated answer in prod. To fix it you need the full trace of that one request, and how fast you can pull it depends entirely on …
Observability for LLM Apps: Tracing, Cost Tracking, and Eval Loops
If you've shipped a traditional backend service, you already know the observability checklist: logs, metrics, traces, alerts. LLM-powered apps need al…
Mastering Production Reliability: Practical Observability with OpenTelemetry, Prometheus, and GitHub Actions
In modern software engineering, traditional monitoring — simply knowing if a system is up or down — is no longer enough. High-velocity engineering tea…
AI Agent Observability Runs on Conversation IDs | Focused Labs
Agent observability gets weirdly polite at the exact moment it should get nosy. It records the model call, stores the prompt, counts tokens, and then …
Can you build observability ingestion on S3 alone — no Kafka, no disks, no coordination layer?
TL;DR — A Kafka + Flink + OTel ingestion pipeline cost us ~$700–800/month at 10 MB/s. We rebuilt it as a single binary where the data, the write-ahead…
AI‑агенты в проде: 6 архитектурных ошибок, из‑за которых они не доживают до запуска
На демо AI‑агент может выглядеть надёжным: вызвать инструменты, собрать ответ и отчитаться об успехе. Но в продакшене быстро …
Шесть недель с agentic AI против фрода в adversarial-системе
Я слишком рано понёс первые результаты в наш продукт. Тогда это выглядело логично: мы прикрутили агентный ИИ к анализу логов и поведения пользователей…