Operations | Monitoring | ITSM | DevOps | Cloud

How will AI reshape managed services? The next chapter of managed services won't be measured by how fast teams react to alerts, but by how well they anticipate and prevent them. ScienceLogic CEO Dave Link and Paul Neiswinger, VP of Global Managed Services at Insight, a leading Solutions Integrator that helps clients solve technology challenges by combining the right hardware, software, and services, discuss the shift from reactive operations to proactive, outcome-driven service, and what it takes for leaders to get there.

AI doesn’t fail because it’s not smart enough—it fails because it can’t see the full picture. In this video, Sterling Parker, Ivanti’s SVP of Technical Solutions and Services, explains why fragmented and "dirty" data is the biggest obstacle holding AI back for organizations today. When AI pulls from disconnected systems, it’s forced to fill in the gaps with its own intelligence, leading to hallucinations and outcomes that are hard to trust. Sterling breaks down how these "cracks in the foundation" can actually create new security vulnerabilities when scaled too quickly.

What if you could counterspell an agent action? GitKraken Desktop 12.4 pulls the whole AI agent workflow into one place, so you stay in the flow. Back in 12.0 we shipped Agent Sessions, where you kick off AI coding agents right inside the context of your repo. GitKraken 12.4 builds on that. What's new in 12.4: This release is not about handing more of your work to agents. It's about seeing everything they do, and deciding what actually changes.

We spend thousands of dollars "token maxing" and running endless debugging cycles just to walk our LLMs through a problem. But is the AI actually failing, or are we just withholding the right environment? Giving your AI assistant its own sandbox to test hypotheses might just be the missing link in your development workflow.

Why Observability Systems Fail AI Code.

OpenTelemetry instrumentation used to mean hours of manual work, wiring up metrics service by service. Now you can just ask for it. Tell an AI agent what you're trying to observe, something like "add OpenTelemetry so I can track this application's performance," and it turns that into an intent specification, then enriches your existing code with instrumentation to match. OpenTelemetry is open source and on GitHub. Pairing it with an AI agent that actually understands your codebase is what turns "add some metrics" into instrumentation that's useful.

We’re back with another roadmap stream! You might think to yourself, “Talking about 2027 already? Isn’t that a bit early? I’m still sweating and lying out by the pool!” But planning this far ahead will give you plenty of time to sort out priorities, talk to relevant parties, set your budget(s), and more. Having these discussions in August may feel extreme but the leaves will fall and the snow will pile up before you know it, so you might as well get started now!

Open source is the backbone of most software. But should developers trust OSS? npm, PyPI, and Maven don't vet packages before publication, so "publicly available" doesn't automatically mean "safe to pull right now." A secure OSS posture is to trust the ecosystem but verify at ingestion: route packages and dependencies through a controlled layer that runs scanning, age checks, and malware detection before anything reaches a build.

For every $1 you pay OpenAI or Anthropic, it's costing them about $1.60. AI is running at a loss — so is the whole business model broken? The full bill for AI hasn't landed yet. In this ShipTalk short, Field CTOs Adam and Martin break down the economics of generative AI: why the frontier labs lose money on every prompt, why they'd need to raise prices ~60% just to break even, and the bet the entire industry is making — that inference costs drop fast enough to catch up. Plus the Gartner forecast every CFO should see: by 2028, the AI bill could be bigger than the employment bill.