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Inside the Gartner Market Guide for CSP Service and Network Assurance Solutions: Agentic AI and the Foundation It Runs On
Most CSP assurance roadmaps now carry an AI line item. Fewer have a clear answer for what that AI actually runs on. Over the past year, the working question across operators and vendors has narrowed to something practical: how to put agents to work in assurance while keeping operators in control.
Why AI Adoption Fails Without Operational Maturity First
Most MSPs are already experimenting with AI in some form, and every vendor at every conference has an AI for MSPs pitch ready. Few have stopped to check whether their own operations are solid enough to scale. Our 2026 IT Trends Report found that three-quarters of IT leaders believe they have an AI policy, while fewer than half of help desk staff agree. That’s the real risk: AI doesn’t fix drift, unclear ownership, or gaps between what’s documented and what’s actually happening.
Monitor Azure Functions across every hosting plan with Datadog
Microsoft’s Azure Functions serverless compute service abstracts away infrastructure management to simplify how teams deploy and operate applications. However, the managed environment can make it harder to understand what happens inside those applications after deployment. Traditional approaches that rely on agents, extensions, and sidecars for direct collection of telemetry data aren’t available across every Azure Functions hosting plan, which can leave gaps in end-to-end visibility.
Build your own Bits Agent with Datadog Bits Agent Builder
Datadog Bits Agent Builder lets you build AI agents that use your observability data to automate operational tasks. In this walkthrough, see how to build an agent that analyzes monitor and alert activity, identifies patterns, and provides actionable recommendations to improve your monitoring strategy. With Bits Agent Builder, you can give agents access to Datadog data and tools, customize their instructions and models, and run them automatically to continuously analyze and act on your environment.
Getting started with ConnectWise PSA dashboards
If you run your service desk on ConnectWise PSA, it's already the system of record for almost everything that matters. Tickets, agreements, configurations, projects, time entries, it's all in there. What it doesn't give you is an easy way to put that data next to the rest of your stack, your RMM, your security tooling, your backup platform, so you can look at a client and see the whole picture rather than one system at a time.
Open Source Is Good Marketing
Summary: Jean-Jérôme Schmidt-Soisson, marketing lead at VictoriaMetrics, shows that open source and marketing are not opposites. Open source itself can be a powerful growth engine through trust, community, content, documentation, and discoverability. The article explains VictoriaMetrics’ pull-based marketing strategy, anchored in open-source values, to support adoption and business growth. This is our second article on the relationship between open source and marketing.
The Difference Between Keeping Up and Catching Up
It is common to find two organizations running the same platform, staffed by people of similar skill, operating under comparable budgets, and arriving at completely different relationships with the technology they depend on. For enterprise IT teams, this often shows up in how they manage platform releases, integrations, upgrades, and ongoing maintenance.
Built to amplify: how Lumen is rethinking teams and technology in the AI era by Greg Freeman | AIFNL
Last year, Lumen shared its AIOps roadmap. This year: two updates. First, how Lumen's thinking on AI-era org design has shifted — including why the instinct to cut junior headcount is a trap, and what sustainable team structures look like instead. Second, what Lumen built as a result: a single AI core (AskGreg) extended into a customer-facing email agent (NORA), a chatbot, and an in-progress voice agent — all leveraging the same modular platform. Live video demos show the system diagnosing network problems from alarm data and driving real-time network decisions. The closing thesis ties it together.
Making AI (net)work: tips for a successful AI-integrated network by Jason Gintert | AIFNL
AI in network operations has moved past the demo. In some shops it's already cutting detection time and clearing the alert noise that used to bury the engineering team. In others, a promising rollout has quietly stalled the moment it met production. That difference between the two rarely comes down to the model, it comes down to how the AI gets integrated into the network and most importantly, the team around it.