Articles by Saurav Kumar

Learn how to test GitHub Copilot Enterprise settings and establish consistent AI coding governance across teams, repositories, and development environments.

Learn how to evaluate Azure Cosmos DB AI query assistance by measuring generated query accuracy, performance, request units, and cost efficiency.

Learn how to benchmark AI-generated unit tests against human-written tests using code coverage, mutation scores, defect detection, false positives, readability, and maintainability.

Learn how to enforce architecture rules on AI-generated .NET pull requests using dependency checks, layering rules, forbidden references, and automated validation.

Benchmark .NET Worker Services under CPU and memory pressure by measuring throughput, queue backlogs, garbage collection, latency, resource usage, and application stability.

Learn how to detect dependency vulnerabilities in ASP.NET Core APIs during CI using automated security scanning, vulnerability gates, package auditing, and DevSecOps practices.

Benchmark Redis connection multiplexing in high-throughput .NET APIs by measuring throughput, latency, connection usage, CPU consumption, and scalability under load.

Learn how to build AI tool approval workflows with Microsoft Foundry using human-in-the-loop controls, permissions, authorization, auditing, and secure agent execution.

Benchmark AI-generated code rework across human and agent workflows by measuring defect rates, review cycles, rework effort, delivery time, code quality, and engineering productivity.

Build architecture fitness functions for AI-generated .NET code to automatically enforce architectural boundaries, dependency rules, layering constraints, design standards, and quality checks in CI pipelines.

Build release gates for AI-generated code in enterprise repositories using automated security, testing, dependency, quality, and architecture checks before production deployment.

Benchmark Redis serialization formats for high-throughput .NET APIs by comparing JSON, MessagePack, payload size, latency, memory allocations, throughput, and performance.

Build automated architecture drift detection into .NET pull requests to enforce architectural rules, identify dependency violations, and prevent unintended design changes in CI.

Benchmark local LLM inference on Windows using quantized models and compare CPU, GPU, and NPU performance, memory usage, latency, throughput, and model efficiency.

Build AI-assisted architecture gates for large .NET repositories that detect architectural violations while keeping deterministic rules, dependency constraints, and governance as the source of truth.

Secure multi-tenant AI agents with OBO token exchange. Preserve user identity, enforce tenant isolation, and implement least privilege.

Design stable, explicit .NET event contracts for AI agents. Ensure clarity, versioning, and provenance for robust event-driven systems.

Benchmark .NET Lambda cold starts with durable workflows. Understand latency, optimize performance, and improve serverless application efficiency.