Operations | Monitoring | ITSM | DevOps | Cloud

sentry

When agents orchestrate agents, who's watching?

Apr 23, 2026 By Paul Jaffre In Sentry
You used to monitor services. Then you started monitoring AI calls inside services. Now your AI agent is spinning up other AI agents to complete tasks. Your old monitoring instincts need to evolve. This isn't hypothetical. Agentic architectures are already in production. Coding agents are calling search agents; orchestrators are spawning specialized sub-agents for retrieval, planning, and execution. Teams are shipping these systems faster than they're figuring out how to watch them.
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Incident.io

What does using AI for post-mortems actually mean?

Apr 23, 2026 By Article In Incident.io
Everyone is using AI to help with post-mortems now. The pitch is obvious: post-mortems are time-consuming, the blank page is brutal, and AI is very good at producing structured, confident-sounding documents quickly. We're not here to push back on that. We've built AI into our own post-mortem experience, pulling your Slack thread, timeline, PRs, and custom fields together and giving your team a meaningful starting point in seconds. We think that's genuinely valuable, and the teams using it agree.
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GPT Image 2 Brings Visual Work Closer

GPT Image 2 Brings Visual Work Closer

Apr 23, 2026 By OpsMatters In OpsMatters
Most AI image tools are easy to praise in a vague way. They can generate striking pictures, imitate styles, and turn a short prompt into something that looks impressive enough to share. But that kind of praise has started to feel cheap. The image model market is crowded now, and "it makes beautiful images" is no longer a meaningful claim by itself.
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cloudzero

What Is LLM Observability? For CFOs And Engineers, The Missing Layer Is Cost

Apr 22, 2026 By Keith MacKenzie In CloudZero
You probably have Datadog. Maybe New Relic, maybe Dynatrace. Your observability stack has been solid for years — and you're still flying blind on AI cost. Here's why LLM observability needs a fourth pillar most tools skip, and how to build one that actually tells you what your models are costing you per request, per feature, per customer.
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cloudzero

Blind Tokenmaxxing Is The New Cloud Waste. Focus on Outcome-Maxxing Instead

Apr 22, 2026 By David Aponovich In CloudZero
Meta's internal token leaderboard sparked a frenzy — and a reckoning. Tokenmaxxing without attribution is just cloud waste 2.0. Companies like Hudl and Duolingo use cost intelligence to connect every AI dollar to a business outcome.
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digitate

Why Enterprise AI Demands More Than Just Automation

Apr 22, 2026 By Digitate In Digitate
Based on insights from The Intelligent Enterprise podcast, “The Evolution from Automation to Autonomy” Every couple of weeks, The Intelligent Enterprise podcast steps away from the day-to-day noise of enterprise life to explore big ideas from a fresh perspective. In one recent episode, the focus turned to a question many organizations are still grappling with: What does it really take to build an AI-powered enterprise that works with people, not against them?
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Episode 10 - How I Learned to Stop Worrying and Love AI

Apr 22, 2026 By Digitate In Digitate
Are we still in the first chapter of AI, and mistaking it for the whole story? In this episode of The Intelligent Enterprise, host Tom Stoneman zooms out from the headlines to explore where we really are in the AI journey. He’s joined by journalist and independent analyst Joe McKendrick, who has spent decades documenting how emerging technologies reshape business and society. As co-chair of the AI Summit in New York and a senior contributor to Forbes and ZDNet, Joe brings the perspective of someone who understands how these stories unfold over time.
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ScienceLogic

The New Economics of Enterprise AI: Why Small Models Win Where It Matters

Apr 22, 2026 By ScienceLogic In ScienceLogic
For years, progress in AI was equated with scale. Larger models, broader parameter counts, and increasingly complex cloud architectures were treated as signals of advancement. In enterprise operations, however, scale alone does not determine success. Economics does. As AI becomes embedded in operational workflows, organizations are discovering that model size is less important than cost stability under continuous load. AI-driven operations do not run in bursts. They run constantly.
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