What KPIs prove the Cursor update improved team productivity without increasing risk?

When you flip on Vertex AI for your org, support teams are suddenly going to get tickets that look very different from the usual login not working stuff. Instead of deep-diving ML internals, the goal is to train support to recognize patterns, give clear responses, and escalate to the right folks fast.

Productivity KPIs (did the update make teams faster?)

• Deployment Frequency- How often teams are shipping new builds compared to before. If pods and apps can roll faster with new pipelines, this should go up.
• Lead Time for Changes- Time from code commit to deployed on OpenShift. If the new update improved CI/CD integrations, this number should shrink.
• Mean Time to Recovery (MTTR)- How quickly a failed deployment rolls back or recovers. A modern OpenShift update (esp. if you got GitOps/ArgoCD features) should make this near-instant.
• Self-Service Actions- No of support tickets for cluster operations (namespace creation, RBAC requests, image builds). If the update added automation/self-service, those tickets should drop.
• Build/Deploy Success Rate- % of builds that succeed first time. A cleaner CI/CD integration after upgrade = fewer broken builds.

Risk/Operational KPIs (prove you didn’t add fragility)

• Change Failure Rate- % of deployments that lead to hotfix/rollback. If productivity went up but this stays flat (or drops), you’ve proven no extra risk.
• Cluster Availability- Measured via SLO/uptime of the control plane and workloads. No degradation means the update didn’t destabilize infra.
• Security Findings- No of image scans or compliance checks failing pre vs post-upgrade. You don’t want to speed things up by cutting corners on CVEs.
• Policy Violations- Count of denied builds/deploys by OPA/Gatekeeper/ACM. If policy adherence stays consistent, risk hasn’t increased.
• Resource Efficiency- CPU/memory per workload vs. baseline. Helps prove you didn’t buy productivity at the cost of runaway infra spend.

To restrict Sourcegraph features to a pilot group, you need to use a combination of native Sourcegraph policy controls and external feature flag management. This allows for granular, user-level control that applies both to Sourcegraph's built-in features and to your codebase, which Sourcegraph indexes.

• Method: Use a feature flag provider

This is the most flexible approach, as it decouples the feature release from code deployment and provides fine-grained control based on user attributes. A provider like LaunchDarkly, Harness, or Flagsmith is recommended.
Setup process:

• Define user groups and attributes. In your feature flag provider, create a new group for your Sourcegraph pilot users. Define attributes that your Sourcegraph instance can query, such as a user ID, email address, or group membership.
• Instrument your code. Use the feature flag provider's Software Development Kit (SDK) to wrap the Sourcegraph features you want to restrict in conditional logic.
• Deploy your code. Deploy the new code with the feature flags in a disabled state. This makes the feature invisible to all users.
• Target the pilot group. In your feature flag provider's admin panel, create a rule to enable the feature flag specifically for the pilot user group. This allows you to turn the feature on or off for the pilot group instantly, without a code redeployment.

To train your support teams for the most likely GoLand issues, focus on the following core areas: project setup, version control, debugging, and performance issues. Create a multi-faceted training plan that includes workshops, a comprehensive knowledge base, and mock-ticket scenarios to ensure your team is well-prepared.

• Identify and categorize top ticket issues
• Develop targeted training modules
• Build a comprehensive knowledge base
• Use hands-on and practice methods
• Establish a feedback loop

October 2025 holiday-related freezes
In many regions, October contains significant national and regional holidays that teams may observe. Deploying changes around these dates is risky because support and development staff may be unavailable.

• October 1–2: Dussehra and Gandhi Jayanti create a major holiday period in India that should be treated as a freeze window. Since October 1 is a Wednesday and October 2 is a Thursday, many teams will take Friday, October 3, off for an extended five-day weekend.
• Mid-to-late October: The Diwali festival period in India, with the main day falling on Monday, October 20, 2025, is another critical time to avoid.
• Check regional calendars: Regional holidays vary, so you should consult local calendars for any other significant observances that could affect your teams or users.

To export JetBrains IDE logs to your Security Information and Event Management (SIEM) system with least-privilege scopes, use a log shipper to monitor the IDE's log directory and forward the files. This approach avoids granting unnecessary permissions to the log collector. The process involves identifying the correct log path, configuring the log shipper with restricted access, and setting up the SIEM to receive and parse the logs.