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Part II: Inside Alert AI Analysis: From a Single-Agent Prompt to an Agent Harness

TL;DR: This is the engineering companion to our announcement post, Upgraded Alert AI Analysis: Automated Incident Investigation, read that one for what the new generation does for your team; read on for how it works under the hood. Interested in hearing more? Book a demo to see the Alert AI Analysis Agent live. Root cause analysis is one of the harshest tests you can give an AI.

Upgraded Alert AI Analysis: Automated Incident Investigation

TL;DR: OrionIQ has launched the next generation of its Alert AI Analysis agent within the Open 360 AI platform, designed to automate and accelerate incident investigation. Key features of this evolution include: Agent-Based Investigation: Instead of relying on a single prompt, the system coordinates specialized AI agents to correlate data across diverse sources like logs, metrics, deployments, and tickets.

Unified Logs, Traces, and Errors: Why One Tool Beats Three

Last updated: July 2026 Your Rails app throws a 500. You open Sentry and find the exception. The stack trace points to a controller action, but it does not tell you why the database call failed. You switch to Datadog and search for the request trace. The trace shows a 3-second query, but you do not know what the application was logging at that moment. You open your log aggregator, paste in the request ID, and scroll through output until you find the slow query log line that explains the lock contention.

When Does a Self-Service Portal Actually Reduce Tickets?

A self-service portal is designed to reduce IT support tickets by enabling employees to solve common issues on their own. But if self-service is supposed to improve efficiency, why do so many portals remain unused while help desk queues continue to grow? In this video, you'll learn what a self-service portal is, why many organizations struggle with low adoption, and the three key factors that determine whether your portal actually reduces ticket volume.

When and what should I be logging?

This is a follow-up to Sergiy’s post Errors, traces, logs, metrics: when to reach for what. Modern observability platforms, like Sentry, give developers a lot of choice. For a given problem, should you use traces, profiles, metrics, logs? If you take away one thing from this post, I hope it’s this: when in doubt, start by adding a few targeted log lines.

Build an SRE Agent Harness for AIOps Without Context Blowout

An agent harness for AIOps is the runtime layer that coding agents like Claude Code were never built to provide: context isolation, decision traceability, and gated execution for tools that touch production. Aura is Mezmo's open-source (Apache 2.0) agent harness, purpose-built for operations work rather than software development.

Claude Code Monitoring at Scale: Gateways and Routing With OpenTelemetry

Chelsea and I recently wrote a guide on how we monitor Claude Code usage internally with Bindplane. TLDR; We remotely manage a Bindplane Distribution of the OpenTelemetry Collector (BDOT) that runs on every engineer's laptop. This setup is great, but it has one downside. Sending to Google Cloud Monitoring, Swarmia, and any other destination directly from an engineer’s laptop is limited to local processing. You can’t get the benefit of centralized routing and processing on a gateway.

The future of governing AI agents

How to build governance into autonomous security agents from the architecture up The industry has moved fast on capabilities. Agents now triage alerts, investigate endpoints, create detection rules, and enrich indicators, and they are even capable of performing most actions we as security operators can perform. The architecture patterns are maturing, as are the models, but governance is not keeping pace.