A practical framework for graduated autonomy in self-healing infrastructure, covering three remediation tiers and policy-driven blast-radius controls for cloud SRE teams.
This traces Java’s evolution from Oak to a global software platform, revealing its impact on open source, standards, engineering, and the community behind it.
Evaluation has real costs (inference spend, latency, storage) — budget for it explicitly, and treat every user-reported regression as a permanent new test case.
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.
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.
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 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.