Network teams are drowning in telemetry across hybrid clouds, making it increasingly difficult to answer simple questions about capacity and security quickly. Kentik AI Advisor solves this by transforming raw network data into actionable, intent-driven advice through a conversational interface.
Moving from data display to intent-driven reasoning
We think the biggest bottleneck in network management isn't a lack of data, but a lack of contextual reasoning.
Most teams assume they need more granular dashboards and alerts to understand their infrastructure.
The truth is, true network intelligence requires an AI layer that can reason about operator intent and recommend decisive action.
Why is hybrid network management so overwhelming?
- Network engineers are forced to manually correlate data across traffic, cloud, and device interfaces to diagnose a single performance issue.
- The constant pressure to balance cost, performance, and security leaves teams with limited resources, ultimately delaying critical strategic initiatives.
- Traditional tools only display raw metrics and graphs, requiring deep domain expertise to translate anomalies into a concrete remediation plan.
What the AI Advisor actually does
The video demonstrates how Kentik shifts the workflow from manual correlation to natural language querying. When a user asks, "Where do I need to add capacity in my network?", the system doesn't just return a raw traffic graph. It analyzes 48 hours of telemetry across CDN interfaces and application traffic, automatically prioritizing the findings into high and medium-priority buckets based on urgency.
Instead of leaving the user to guess the next step, the AI generates specific action items, like upgrading particular CDN cache interfaces or planning capacity expansion for precise WAN links. It seamlessly handles security contexts as well, allowing users to ask about DDoS vulnerabilities and receiving an instant breakdown of exposed devices and active alerts, transforming complex analysis into simple, decisive actions.
To scale network operations effectively, monitoring tools must evolve past simply displaying data and start reasoning about the infrastructure to deliver precise, actionable advice.
How is your infrastructure team currently bridging the gap between raw telemetry and actionable capacity planning across hybrid environments?
Top comments (1)
One architectural edge-case missing above is managing user trust during the asynchronous latency between a natural language query and the complex LLM response. The interface must earn credibility organically, leveraging continuous motion to expose the off-screen telemetry correlation rather than hiding it behind a static loading spinner. When mapping this out at Advids, our baseline is proof-first messaging: the UI secures user confidence immediately, mapping hidden database queries into on-screen visual proof to resolve the tension between claiming an insight and actually showing the work. The platform validates its own intelligence autonomously, utilizing visual state changes to prove the backend is actively routing data instead of just returning canned text. Are your AI loading states engineered to prove the underlying calculation, or are they just masking latency with generic animations?