Riya Patel's contributions

Prepare .NET workloads for Kubernetes 1.34 maintenance by testing deployments, compatibility, container behavior, and upgrade readiness before the maintenance transition.

Benchmark migration from SQL Server Express to Azure SQL and explore compatibility, performance, configuration, and application changes when moving .NET workloads.

Learn how to build persistent .NET services with Aspire 13.5 and Kubernetes volumes while testing storage persistence across container restarts and deployments.

Learn how to benchmark AI-generated database queries against expert-written SQL by comparing correctness, performance, cost, maintainability, and execution efficiency.

Learn how to control AI agent costs when querying Azure Cosmos DB by monitoring request units, limiting expensive operations, and setting usage safeguards.

Learn how to test AI agents when web search and external sources fail, including timeout handling, fallback strategies, graceful degradation, and response validation.

Learn how to benchmark AI tool search accuracy across large enterprise tool catalogs by measuring selection accuracy, latency, token usage, and incorrect tool calls.

Learn how to build automated AI code review pipelines with GitHub Copilot Agent Plugins, repository-specific tools, coding rules, and automated review workflows.

Learn how to track and attribute AI agent usage and costs across GitHub teams using agent-level metrics to improve visibility, governance, and FinOps.

Learn how to enforce architecture rules on AI-generated .NET code using automated fitness tests for dependency direction, layering, forbidden references, and maintainability.

Learn how to use Azure Developer CLI to test repeatable AI infrastructure deployments with automated provisioning, configuration consistency, environment management, and DevOps workflows.

Learn how to test AI agent tool selection with large tool catalogs using Microsoft Agent Framework, measuring selection accuracy, latency, reliability, and tool discovery performance.

Learn how to build reproducible AI evaluation datasets for enterprise agents using versioning, scenario coverage, ground truth, regression testing, and measurable quality metrics.

Build automated .NET dependency risk reports from NuGet auditing to identify vulnerable packages, outdated dependencies, security risks, transitive dependencies, and software supply chain exposure.

Benchmark .NET 11 HTTP compression for large JSON APIs by measuring response size, bandwidth savings, CPU usage, latency, throughput, and compression efficiency across realistic workloads.

Build data residency controls for global AI workloads by managing geographic processing, data zones, model routing, latency, compliance requirements, and regional AI infrastructure.

Test AI agents with synthetic enterprise documents to evaluate extraction accuracy, grounding, hallucinations, edge cases, document variations, and system reliability under controlled conditions.

Build automated evaluation gates for AI agents to detect tool-calling regressions when prompts, schemas, models, or agent workflows change across development and deployment.

Measure AI agent token waste caused by poor tool descriptions and evaluate tool-selection accuracy, prompt efficiency, latency, token usage, and inference costs.

Test AI coding agents against dangerous infrastructure changes using adversarial scenarios for Terraform, IAM, databases, deployments, permissions, and destructive cloud operations.

Benchmark LLM model-routing strategies by comparing inference cost, response latency, answer quality, reliability, and workload-specific model selection across multiple models.

Build trace-based AI evaluation gates in CI/CD to detect quality regressions, enforce evaluation thresholds, analyze model behavior, and prevent unreliable AI releases.

Measure local AI inference performance across CPU, GPU, and NPU hardware to compare latency, throughput, power efficiency, and hardware acceleration capabilities.