We’re proud to announce the general availability of IBM Cloud Pak for AIOps 4.10. This version introduces key capabilities that reflect what today’s enterprise operations teams need most: smoother transitions from earlier solutions, better context around real-world events, cleaner topology views, and smarter, more explainable automation.
Migration from Netcool WebGUI to Cloud Pak for AIOps
One of the biggest hurdles for teams moving to Cloud Pak for AIOps has been preserving the years of work put into customizing filters and views in the Netcool WebGUI. With version 4.10, we’re addressing that directly through a new migration capability. This release introduces a script-based solution that can extract and translate those existing filters, bringing them into CP4AIOps with minimal friction. For users, this means more than convenience—it preserves hard-won institutional knowledge and speeds up the transition to a modern AIOps experience. The result is a far smoother onboarding process that lets teams start benefiting from AI-driven event handling and intelligent correlation without needing to rebuild their environment from scratch.
Geospatial Visualization of External Risks
In 4.10 we’ve introduced the ability to visualize geographically-distributed external risks, such as wildfires, and their potential impact on infrastructure. The idea stemmed from a real operational challenge—teams were dedicating personnel to manually monitor multiple external data sources (like fire maps, wind speeds, and weather alerts) and mentally correlate them with known issues. Cloud Pak for AIOps now brings this capability directly into the platform by ingesting fire risk data from IBM EIS and NASA FIRMS and overlaying it on the network topology. When a datacenter is in proximity to an active wildfire zone, the platform can now surface that context automatically, helping operations teams make better decisions about whether an outage is likely to be caused by environmental events, or whether it’s safe to dispatch a field crew. Beyond fire data, this external risk observer lays the groundwork for additional integrations—such as floods or storms—unlocking the potential for highly contextual, real-world-aware incident management.
Detection of Single Points of Failure in the Topology Viewer
Infrastructure maps are only as useful as the insights they generate. With 4.10, we’ve enhanced the topology viewer to automatically surface single points of failure—those fragile architectural junctures where multiple critical services depend on a single resource. This new analytic capability helps teams proactively identify design risks before they cause outages. For example, if several customer-facing services rely on a single internet-facing node, the system will flag that node as a single point of failure, prompting action. This enhancement is also a foundational step toward intelligent impact analysis, a future capability where Cloud Pak for AIOps will be able to model potential actions, assess their risk, and automatically take the safest path forward. In the meantime, this visualization gives operations teams a powerful way to increase resiliency and reduce business risk through smart topology design.
Topology Cleanup and Merging in the vCenter Observer
Clarity is crucial in large-scale topology models, and in past versions, the vCenter observer created unnecessary visual noise by importing low-value child resources—like CPU instances or operating systems—as standalone nodes. These elements often had no operational utility outside of their parent VMs, leading to a cluttered and harder-to-navigate topology. In 4.10, users now have the option to merge those child resources into their respective VMs. This significantly reduces visual clutter while preserving the most important data as properties of the parent. The result is a cleaner, more intuitive topology that helps operators focus on what matters most—without getting lost in irrelevant detail.
Enhanced Interface Data from SevOne Observer
Interfaces are where much of the action happens in network operations, but up until now, our SevOne Observer provided limited metadata for those components. With 4.10, we’ve expanded the observer’s data collection to include additional key fields: Object Alternative Name, System Description, and User Description, as well as explicit device association for each interface. This richer context makes it easier to understand what each interface does and what device it belongs to—crucial details when diagnosing issues or writing automation policies. These enhancements directly support more accurate event correlation and policy targeting, ultimately leading to more responsive and intelligent network operations.
General Availability of Multi-Zone High Availability
High availability gets a major upgrade in this release with the GA of multi-zone deployment support. Until now, Cloud Pak for AIOps could tolerate single-node failures in a given cluster, but multi-zone support raises that bar significantly by enabling the system to withstand the failure of an entire availability zone. Whether due to a power loss, network outage, or other localized incident, CP4AIOps now ensures continuity by distributing workloads across three separate zones. This is especially critical for industries with stringent uptime SLAs or sensitive workloads, and is part of our long-term goal to support the highest level of resiliency in our deployments.
Conditional Branching in Runbooks with Explainability
As automation becomes more complex, visibility into how it operates becomes non-negotiable. In version 4.10, we’ve introduced enhanced explainability into runbook execution, particularly when conditional logic is used. Now, administrators can see which conditions were evaluated, which paths were taken, and why. This feature makes it far easier to debug and fine-tune automations, especially in regulated environments where traceability and justification are required. It also builds confidence in automation adoption, since users can now understand—not just trust—how and why certain actions were taken by the system.
Support for Custom Fields in Policy Justification
Many enterprise environments rely on custom object server fields to align AI-driven policies with organizational realities. In 4.10, we’ve added the ability to surface those fields—like Technology, RegionId, and Municipality—in the policy justification UI. For clients these fields are essential to validate the policy is working as expected. By making these custom fields visible, we ensure that users can not only verify but actively tune the behavior of policies to fit their operational models, reinforcing trust and improving governance.
Looking Forward
Cloud Pak for AIOps 4.10 delivers more than just new features—it delivers deeper context, greater operational clarity, and smarter automation that aligns with how teams actually work. Whether you’re responding to wildfires, migrating systems, or scaling automation across zones, this release gives you the tools to make better decisions, faster—and with more confidence.