Secure MCP servers against prompt injection, data leaks, and denial-of-wallet with four practical, OWASP-aligned gates from code to production. Runnable code.
Learn how to engineer production-ready AI agent context with a five-stage pipeline for retrieval, enrichment, verification, compression, and prompt injection.
Practical patterns, code examples, and hard-earned lessons from implementing enforceable guardrails around autonomous agents in real enterprise environments.
Learn key concepts like embeddings, vector search, chunking, hybrid search, semantic ranking, and how these pieces fit into a scalable, enterprise-grade AI application.
Learn about why QA-as-a-phase persists, the costs it creates, and how teams can transition to continuous quality across the software development lifecycle.
Explore a three-part field manual on building reliable production AI agents through context engineering, guardrails, and human-in-the-loop architecture.
AI's output is untrusted input. Validate it, fence it in, and never hand it raw to anything that can do damage — the same reflex you already have for form data.
Learn how to choose the right API testing framework, including REST Assured, Supertest, pytest, Postman, Karate, and Keploy, for better API test automation.
Learn to build a strong testing pipeline to ensure code quality, as in enterprises today most code is generated by AI agents at a huge volume and scale.