What you think before
you build determines
what gets built.

Agentic engineering is not vibe coding. It's a disciplined SDLC: context files, plan-first workflows, worktrees, quality gates, and MCP governance.

Production AI isn't a demo. Once it touches customers, data, and revenue, you need architecture, evals, observability, security, and incident response.

The production agentic AI stack: tool calling, MCP, planner/executor patterns, multi-agent design, memory, sandboxing, approval gates, and eval loops.

Enterprise RAG deep dive: chunking, hybrid search, reranking, GraphRAG, agentic retrieval, evaluation, and the governance layer most teams overlook.

Prompt engineering was the first wave. Context engineering is next: designing memory, retrieval, tools, structured outputs, and evals around the model.

A plain-English walkthrough of how LLMs work — tokenization, transformers, RAG, agents, and evaluation — using software architecture analogies.

AI is moving beyond chatbots. Reasoning models, multi-agent orchestration, agentic RAG, MCP, and memory are forming the enterprise AI operating layer.

As agents take on real work, reliability becomes the core challenge. The future of AI isn't better models — it's better harnesses around them.

DORA 2023 isn't just speed — it's reliability, feedback loops, and DevEx. The five core metrics plus supporting indicators that surface CI/CD friction.

Key takeaways from Designing Data-Intensive Applications: reliability, scalability, OLTP vs OLAP, encoding formats, protocols, and deployment patterns.

Software projects fail from unclear goals and unmanaged scope, not weak coding. Planning, estimation, risk, quality, and team leadership — grounded in SWEBOK.

How DevOps transforms delivery through automation, collaboration, and continuous improvement. Core practices, the CAMS model, real tools, and pitfalls.