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Home » QA practices

QA practices

How to Test Your AI Apps and Features: A Comprehensive Guide for QA Leaders

By Dhimil Gosalia|2026-02-10T07:26:13-05:00February 10, 2026|

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What Is Your Platform Engineering 2.0 Platform Made Of?

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Which description best matches your role? (Select one)*
- Platform engineering
- DevOps, DevSecOps or SRE
- Software engineering
- Cloud or infrastructure engineering
- Enterprise architecture
- Data science, ML engineering or MLOps
- Security, risk or compliance
- FinOps
- Engineering or IT leadership
- Consultant or technology vendor
- Other

What is your organization's current platform engineering status? (Select one)*
- Operating a mature internal platform
- Operating a platform that is still expanding
- Building our first internal platform
- Evaluating or planning a platform initiative
- Operating multiple purpose-built platforms
- No current platform engineering initiative
- Unsure

What are the primary outcomes expected from your platform? (Select up to three)*
- Improve developer productivity
- Accelerate and standardize software delivery
- Improve security, compliance or reliability
- Control infrastructure, cloud or AI costs
- Support AI/ML development and operations
- Govern AI models and agents
- Modernize traditional applications
- Enable hybrid or multicloud operations
- Meet data sovereignty or residency requirements
- Other

What is your platform primarily made of? (Select all that apply)*
- Backstage
- Another internal developer portal or service catalog
- APIs, command-line tools or agent interfaces
- CI/CD, GitOps or infrastructure as code
- Self-managed Kubernetes
- Managed public-cloud Kubernetes
- VMware Cloud Foundation or VMware vSphere
- Another private-cloud or virtualization platform
- Public-cloud infrastructure and managed services
- Virtual machines managed outside Kubernetes
- Serverless or application platform services
- Bare-metal infrastructure
- Multiple platforms without a common control layer
- Unsure
- Other

How is your organization supporting AI workloads? (Select one)*
- Extending our existing platform to support AI
- Building a separate AI/ML platform
- Connecting our platform to external model APIs
- Using a managed public-cloud AI platform
- Operating a private AI platform
- Combining external services with internally hosted models
- Supporting AI experimentation without a formal platform
- Not currently supporting AI workloads
- Unsure

Which AI-specific platform capabilities are available today? (Select all that apply)*
- AI gateway or unified model access
- Model registry, evaluation or serving
- AI-specific observability
- Token or inference cost accounting
- GPU provisioning, scheduling or optimization
- Agent identity and permissions
- Agent behavioral guardrails and auditability
- MCP server discovery or governance
- Data access, lineage or residency controls
- None of these
- Unsure

How mature are your AI governance and cost controls? (Select one)*
- Embedded across the platform, with clear ownership and cost attribution
- Partially centralized, but important gaps remain
- Managed separately by individual teams
- Primarily applied after deployment or after costs are incurred
- No consistent governance or cost-control model
- Not applicable
- Unsure

What is the greatest constraint preventing your platform from delivering what the business now expects? (Select one)*
- Platform adoption or developer experience
- Orchestration and integration
- Kubernetes or workload management
- Infrastructure or GPU availability
- Data access and governance
- AI model or agent management
- Security or compliance
- Observability
- Cost visibility and FinOps
- Platform skills or staffing
- Funding or executive support
- Difficulty demonstrating platform value
- Other
Optional: What is the one capability you most need to add or improve?

Would you be willing to participate in a short follow-up interview? (Select one)*
- Yes
- No
If yes, please share your contact information (name, email, and company) so we can reach out.