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.
In the 2026 Gartner® Market Guide for CSP Service and Network assurance, Gartner states: “Generative AI and agentic AI are emerging as key enablers, accelerating the expansion of expectations from AIOps to Agentic Ops.”
It is specific about where assurance teams are putting that energy: “The biggest shift currently taking place in the assurance market is to support increasing levels of network automation and introduce AI agents into assurance capabilities to deliver automation.” Assurance is taking on a heavier mandate, reasoning toward the next action, under governance, with agents participating in the loop.
Where Selector is named
Selector Software is named in the market direction of the 2026 Gartner Market Guide for CSP Service and Network Assurance Solutions.
We believe Selector is referenced in this context because of where our approach is strongest: data-driven correlation and cross-domain root cause analysis that turn high-volume network and service telemetry into explainable answers operators can act on. That foundation is the layer that Selector was designed to provide.
Agentic Ops runs on the OSS data layer
In Agentic Ops discussions, the agents get the attention. The data and correlation layer beneath them, which quietly decides whether those agents produce anything useful, gets far less. Gartner states: “Establish a single, trusted data foundation with clear lineage and quality controls to ensure reliable analytics and AI/ML inputs.”
Anyone who lived through earlier OSS programs will recognize the principle. Lineage and quality were never glamorous, and they were always the difference between an assurance platform that operators trusted and one they worked around.
An agent reasons only as well as the context it can see. Domain-siloed telemetry yields fast, low-confidence output. Context that spans network and service domains, including the relationships between them, gives an agent enough to localize a fault and explain how it got there. The valuable work in assurance happens before any agent acts: correlating events across domains, separating cause from effect, and assembling the evidence an operator or an agent needs to move safely. That investigation-and-coordination layer is where the real difficulty and the real payoff sits. Automation is only ever as dependable as the layer it is built on.
A sequence that earns trust
The most autonomous option is rarely the fastest to value: Gartner says, “CSPs are embracing AI and Agentic AI but remain cautious about full automation, with many preferring AI agents that augment human decision making rather than replace it.”
That instinct holds up in assurance, where a wrong automated action can touch an SLA.
A practical sequence:
- Fix the foundation first, investing in correlated, cross-domain data with clear lineage before agents enter the picture, since everything downstream inherits its quality.
- Pick near-term, measurable use cases such as predictive fault management, root cause analysis, and SLA-relevant detection, which deliver value early and build credibility for what follows.
- Keep operators on the loop, making the AI’s reasoning explainable so they can validate it, and letting confidence grow from a record of recommendations that hold up.
Agentic AI will keep moving deeper into assurance over the next few years. The operators who get the most from it will have put the data and correlation foundation in place first, so every agent they add has something solid to reason over.
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Required disclosures
Gartner, Market Guide for CSP Service and Network Assurance Solutions, Susan Welsh de Grimaldo, Amresh Nandan, Will Rice, 5 January 2026.
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