Agentic AI is not just autocomplete — it is an AI system capable of autonomously executing multi-step tasks. This article breaks down the five phases of the SDLC, explaining where to plug in agents at each phase, how to progress from CLI tools to full-pipeline automation, and the most valuable external resources to track right now.
Agent CLIs are not smarter autocomplete tools -- they are AI agents that can read your codebase, execute multi-step tasks, and operate in real environments. Claude Code, Codex CLI, Gemini CLI, OpenCode, Aider, Pi, Kiro, Amp, Cursor CLI... the tools keep multiplying, but they all share a common set of design principles -- understanding these principles is how you actually get good at using them.
AI engineering has gone through three phases: Prompt Engineering (write better instructions) → Context Engineering (feed the right information) → Harness Engineering (design the entire working environment). Each evolution doesn't replace the previous one — it operates at a higher level of abstraction.
Context Engineering is the core concept that replaced Prompt Engineering in 2025: the focus shifted from 'how to ask' to 'what information to provide.' Delivering the right information at the right time into the context window is more effective than upgrading to a stronger model. This post covers the definition, four key strategies, practical techniques, and common failure modes.
A complete study guide for Claude's official architect certification: five exam domains, six scenario types, common anti-patterns, and hands-on preparation strategies.
An AI agent is not a black box — it is built from three layers: what it knows (Context), how it thinks (Cognition), and what it can do (Action). Understanding these three layers is the key to grasping why agents are sometimes brilliant and sometimes go off the rails, and how to design a truly effective agent system.