Operations | Monitoring | ITSM | DevOps | Cloud

honeycomb

Evaluating Observability Tools for the AI Era

Mar 12, 2026 By Kale Bogdanovs In Honeycomb
Every observability vendor has an AI story right now. Most have an MCP. Many have a chatbot. All have a demo where the AI finds the root cause of an incident in thirty seconds and everyone in the room nods. In the context of a public demo, these tools look almost identical. Ask the AI a question, the tool returns an answer, and the engineer fixes the bug. Impressive. But if you buy based on the demo, you may end up with an AI layer that looks great on a call and disappoints in production.
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onpage

The Hidden Cost of AI Productivity: When Efficiency Turns Into "Brain Fry"

Mar 12, 2026 By Ritika Bramhe In OnPage
A new HBR study reveals that the race to build and manage AI agents may be pushing knowledge workers toward a new form of cognitive overload. If you spend any time on LinkedIn these days, you’ve probably seen the same type of post over and over. Someone proudly announces they built an AI agent that now writes their emails, analyzes data, drafts presentations, and maybe even ships code.
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How Developers Build a Meaningful Career in the Age of AI

Mar 12, 2026 By GitKraken In GitKraken
What does a meaningful developer career look like in the age of AI? We brought together four experts to answer exactly that. In this GitKon panel, GitKraken CMO Kate Adams moderates a conversation with Leon Noel (Managing Director of Engineering, Resilient Coders), Danny Thompson (Director of Technology and host of The Programming Podcast), Maggie Hunter (Recruitment Lead, GitKraken), and Dimitry Fonarev (CEO, Testkube) to explore how software engineers can future-proof their careers, grow their skills, and navigate an industry that is changing fast.
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ScienceLogic

Why Generic AI Fails in Ops: What Trustworthy Actually Requires

Mar 12, 2026 By ScienceLogic In ScienceLogic
Enterprise operations reached a point where complexity outpaced human interpretation and outgrew the capabilities of generic AI. As environments became more distributed and interdependent, every incident, anomaly, and degradation produced ripple effects across systems that require context, lineage, and reasoning. Yet most AI models were not built for this reality. They were trained for general knowledge tasks, not the deeply connected operational truths that define enterprise performance.
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lightrun

How to Reduce MTTR with AI-Powered Runtime Diagnosis

Mar 12, 2026 By Lightrun Team In Lightrun
Reducing Mean Time to Resolution (MTTR) in production systems requires understanding failure behavior in real time. While AI code agents significantly accelerated software development and deployment, incident resolution has remained constrained by incomplete pre-captured telemetry. AI SRE tools improve signal correlation, but MTTR reduction requires runtime-verified diagnosis that confirms execution behavior directly in production systems.
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