What's New at Cribl 4.18: On release days, we wear teal.
In this episode, Leon explores a bunch of BYO (bring your own) enhancements to Cribl including the ability to bring your own AI model, storage, and more.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
The latest News and Information on Observabilty for complex systems and related technologies.
How we made a SQL query optimization agent 59% more accurate using autoresearch and LLM Observability
Without experiment infrastructure to help you test your LLM applications, every research session starts with the same questions: What have we tried previously? What were the numbers? Which prompt version produced that result? Why did we discard that approach? The answers live in scattered notes, terminal history, and half-remembered conversations. Each handoff between sessions loses context. In practice, iteration can slow down as teams get bogged down in testing and analysis.
Honeycomb Canvas: The Multiplayer Workspace for the Agentic Era
Last week, we launched a major update to Canvas, our investigation workspace. The new Canvas has evolved from an AI co-pilot you chat with to a place where your whole team, human and agent, can work the same problem on the same surface. Auto-investigations begin the moment a trigger, SLO, or anomaly fires. Custom skills encode your team's runbooks so every agent investigates with your team's expertise built in.
Unlock telemetry value with a well-planned data lake
Your SIEM only holds a slice of your telemetry. Your data lake holds the rest. We'll show you how to use that to your advantage for investigations, threat hunting, and reporting. Why your data lake beats your SIEM for investigations – Your SIEM keeps a short window of expensive, filtered data. Your data lake keeps everything. When something goes wrong, that difference matters more than you think Threat hunting without the handcuffs – Hunting across months of data in a SIEM is painful and costly. We'll show you how a well-planned lake makes broad, deep searches practical and affordable.
AI Observability In 2026: What It Is, The Five Pillars, And Why Cost Is The One Everyone Skips
AI observability covers performance, quality, reliability, safety, and cost. Most tools handle the first four. Here's what each pillar means, which tools cover which, and why cost is the dimension enterprises keep missing.
AI- Powered Anomaly Detection in Observability
Your dashboards are green. Your thresholds are calm. Then a cascade failure starts and you don't know until users flood your status page. Traditional monitoring is reactive by design. Anomaly detection in observability changes that equation entirely.
Agent Timeline: The Flight Recorder for Your AI Agents
Last week, we introduced Agent Timeline, a powerful new observability experience purpose-built for debugging AI agent workflows in production. Agent Timeline uniquely connects AI-layer visibility to full-stack observability by organizing telemetry around an agentic conversation. A conversation contains one or more agent executions, each of which may contain LLM calls, tool invocations, handoffs, retries, human escalations, and downstream system calls.
Get Lightrun AI Skills: Expert Workflows for AI Agents
Today we’re launching Lightrun AI Skills, structured, repeatable investigation workflows built for AI coding agents. With Lightrun MCP, agents like Claude Code, Codex, and Cursor can already instrument live production services and reason over live runtime evidence without a redeployment. But AI agents remain non-deterministic by design, using the same tool differently every session.
How Honeycomb Is Embracing the Challenges of End-to-End Observability with Embrace
Customers regularly come to us looking to solve their observability problem by connecting the dots from frontend to backend. It sounds straightforward in theory, but in practice it's one of the hardest problems in modern application monitoring. The frontend monitoring tools they already have in place tend to be proprietary or narrowly scoped to frontend needs, leaving them without the context-rich backend data that makes real triage possible.
Cribl Notebook templates in Cribl Search
Investigations are time-sensitive, and analysts shouldn’t waste time recreating the same workflows or rewriting familiar queries. Whether troubleshooting infrastructure, investigating suspicious IPs, or analyzing host activity, teams often rely on duplicating old processes and copying query snippets — a slow, inconsistent approach that’s hard to scale.