Evaluation has real costs (inference spend, latency, storage) — budget for it explicitly, and treat every user-reported regression as a permanent new test case.
As Kubernetes deployments expand across hybrid and multicloud environments, permanently provisioned infrastructure becomes an expensive default. Here's how scale-from-zero aligns capacity with actual demand instead of worst-case scenarios.
Build reliable PySpark pipelines with techniques for data validation, schema evolution, transformation design, partition management, and operational monitoring at scale.
A look at the Open Agent Management Protocol (OpAMP) that has been created by the CNCF OpenTelemetry project and how it could deliver beyond OTel's needs.
Legacy VMware on-prem, reactive AWS, and a ticket queue that never emptied — we deployed agentic AI across both substrates and changed how the team operates entirely.
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.
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.
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.
CI optimization using Git diff and JGit to selectively run impacted Karate tests, reducing regression execution while preserving safe fallback coverage.
AI agents can take over the first minutes of incident response, but only with the right boundaries. Seven guardrails that keep an SRE agent from becoming the outage.
CORE
