OrionIQ Now Connects to 600+ Platforms: Full Context for Agentic Observability
AI agents are only as effective as the context they can access. The problem is that most of that context lives outside your observability platform.
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Observability's Sixth Sense: Grounding Anomaly Detection in Reality
Summary: Machine learning-based anomaly detection improves observability by learning normal system behavior instead of relying only on static thresholds. This article explains how vmanomaly, its MCP server, purpose-built skills, and an LLM-powered UI copilot help engineers explore telemetry, investigate anomalies, build MetricsQL queries, select suitable models, apply business constraints, and validate configurations through natural language.
AI Model Drift: How to Keep Models Reliable
AI model drift is when an AI system's performance and accuracy degrades over time because the data, user behavior, or business environment has changed since the model was trained or evaluated. Even if latency, uptime, and infrastructure metrics remain healthy, model quality can quietly decline, leading to less accurate predictions, inconsistent responses, and reduced user trust.
Introducing AI BubbleUp
BubbleUp has always been the fastest way to figure out what a group of outliers have in common. Draw a box around a band of slow traces, a cluster of errors, or any set of events you're interested in, and BubbleUp compares that selection to the baseline across every dimension you've sent us. It's how Honeycomb users find the "unknown unknowns" that dashboards can’t show you.
DevOps and CI/CD Practices for Scaling Mobile App Backends in Fintech
Fintech mobile applications operate in an environment where performance, reliability, and security are equally important. A slow payment request, an API outage, or a failed deployment can affect thousands of transactions and quickly undermine customer trust. As a product grows, its backend must handle rising traffic without sacrificing stability.
Signal vs. Spend: Building Cost-Aware Observability at Slack - O11yCon 2026
It started with a single log line taking up a massive amount of volume: 500 million emissions per hour. Pulling that thread led Emma and Steven into Slack's broader logging pipeline: 311 billion logs per day at 4.4M/sec peak, with no volume limits, no per-service attribution, and no feedback to the teams generating the noise.
Building an AI Observability Agent: Lessons from the Trenches - Stripe at O11yCon 2026
Stripe shares lessons from building an incident investigation agent, from context-window blowups to why the final 5% still needs a human. In this O11yCon 2026 talk, they dig into what it takes to go from 'it works' to 'it works reliably,' including how pointing agents at like Honeycomb's speeds up on-call investigations.
AMA Recap: More Answers From the Observability Engineering Authors
Last week, we sat down with the authors of Observability Engineering for a live AMA. We ended up getting so many questions (pre-submitted and live) that we couldn't get through them all. Charity, Liz, George, and Austin kindly stuck around afterward to answer more, ranging from low-hanging observability fruits and telemetry to AI and what software engineers can do that Claude can't. Missed the live session? Watch it on demand now.
Running LLM Workloads in Production: An Operations Playbook for Teams That Did Not Sign Up for This
Somewhere in the past two years, AI quietly became an operations problem. The proof of concept your product team shipped - a support-ticket summarizer, a natural-language search box, a code-review assistant - graduated into a production dependency, and now it pages you. The failure modes are unfamiliar: latency distributions with tails measured in tens of seconds, upstream providers that throttle without warning, costs that scale with user enthusiasm rather than infrastructure size, and outputs that can be wrong in ways a health check will never catch.