Operations | Monitoring | ITSM | DevOps | Cloud

honeycomb

Introducing AI Ecosystem: Zoom Out to See Your Whole AI Agent Fleet

Sep 29, 2026 By Dan Juengst In Honeycomb
A few months ago, we launched Agent Timeline to close the gap between knowing an agent failed and understanding why. It took the tangled reality of a multi-agent, multi-trace workflow and rendered it as a single, readable conversation: every LLM call, tool invocation, handoff, and downstream system span laid out in the order they happened.
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upsun

Your AI stack will change again. Stop rebuilding it.

Sep 29, 2026 By Upsun In Upsun
The model your team relies on today is unlikely to be the one you're relying on a year from now. If your team's process for shipping AI-assisted code is built around a specific model, coding assistant, or a vendor's take on an autonomous agent, you are not building infrastructure. You are building something you will tear out and rebuild the next time the leaderboard shifts.
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iOPEX

How AI is Changing Marketing ROI Measurement for CMOs

Sep 29, 2026 By iOPEX Technologies , In iOPEX
When profits miss plan, marketing is the budget line most likely to be cut. The CMO Survey found that marketing expenses are cut 45.4% of the time in that scenario, more often than any other expense category. Most marketing teams have more data than ever. Finance still discounts much of it, because few of those numbers connect spend to business outcomes. The pressure is structural. Gartner’s 2026 CMO Spend Survey shows marketing budgets flat at 7.8% of company revenue.
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rollbar

With Agent Automation, Nobody Has to Start Root Cause Analysis by Hand

Sep 29, 2026 By Rollbar In Rollbar
Until now, someone on your team had to start every root cause analysis in Rollbar. An alert fires, they open the item, read enough of the stack trace to decide it deserves an investigation, and start the analysis by hand, after dropping whatever they were doing. With Agent Automation, Resolve, Rollbar’s AI agent for root cause analysis and fixes, takes over that step. You write a rule once in Project Settings, and when an error matches it, Resolve starts the analysis on its own.
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What is the Grafana Knowledge Graph and How Does It Help AI Agents? (Demo)

Sep 29, 2026 By Grafana In Grafana
Grafana's Knowledge Graph builds a contextual layer on top of your telemetry data, automatically extracting entities and relationships so you can see how your services actually connect. In this deep dive, Jia show how it powers root-cause investigation both in the Grafana UI and through agentic workflows using gcx and Claude Code. Learn more about Grafana's observability tools and try Knowledge Graph for your own telemetry data at grafana.com.
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coralogix

The new APM experience: every service monitored from day one, and ready for your AI agent

Sep 29, 2026 By Coralogix Team In Coralogix
Coralogix APM has told you which service is behind the latency. From today, the new APM experience tells you what is behind the service: the release, the environment, or the query one layer down. It watches every service you run from the moment it appears, without anyone writing an alert. And everything you see on the screen, your AI agent can use too: in Olly, from the Coralogix CLI, or through MCP in your IDE. A latency chart shows that something got slow.
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almaden ai

Collective IQ Business: meet the artificial intelligence that transforms IT management

Sep 28, 2026 By Almaden AI In Almaden AI
The employee digital experience (DEX) is no longer just a concept; it has become a concrete discipline supported by specialized tools. At the center of this transformation is Collective IQ, Almaden's DEX solution, available in the Essential and Business editions. The Business edition includes AlmaAI a family of generative AI capabilities that take IT management to a new level. These capabilities allow IT administrators to simplify their analysis and faultdetection processes, providing everything from daily prioritization to predictive analytics and naturallanguage interaction.
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gitkraken

Models Are Getting Really Good At Git

Sep 28, 2026 By Chris Griffing In GitKraken
I built GitBench because I was curious about how well models did git things. It turns out that it was a good idea because some models will surprise you. Some of the frontier models were remarkably bad and some cheap open models punched above their weight class. But, as they say, “the times they are a changin’”. Before I go too deep into the latest results, you may want to read up on GitBench and what it is and why I built it in the previous blog post.
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