Goal before prompt
Describe the intended outcome, boundaries and acceptance criteria. An elegant prompt cannot compensate for an unclear goal.
AI · AGENTS · SOFTWARE
From model foundations to reliable AI systems.
A practical learning area for AI engineering: models, grounding, agents, evaluation, production, machine learning, Codex and Claude Code.
Last reviewed · 22 August 2026
AI is changing more than how code is typed. Agentic development tools can inspect a codebase, create a plan, change several files, run tools, interpret failures and continue until the result can be verified. The developer’s most important work therefore shifts from producing every character to defining goals, shaping context, designing boundaries and judging the evidence.
This area is about the disciplined form of AI development: clear tasks, controlled autonomy and testable outcomes. It is not a tools contest between Codex and Claude Code. The same foundations determine quality whichever agent is used.
AI engineering learning path
A complete guide to LLM foundations, grounding, agentic systems, evaluation, production operations and machine-learning foundations.
Start the learning path →02Agentic development
Understand the agent loop, context architecture, tools, degrees of autonomy, evaluation-driven development and safe delegation.
Learn the foundations →03OpenAI Codex
Surfaces, AGENTS.md, skills, MCP, code review and a verifiable workflow from task to completed change.
Open the Codex guide →04Claude Code
CLAUDE.md, planning, hooks, MCP, subagents, permissions and Anthropic patterns for agentic coding.
Open the Claude guide →05YouTube and expert voices
Official channels and a curated selection of creators covering agentic coding, AI engineering and model fundamentals.
Find channels to follow →Principles that endure
Context is architecture
Repository rules, relevant files, examples, tools and previous decisions determine what the agent can actually achieve.
Verification is the product
Tests, builds, static analysis, evals and human review make the agent's work inspectable.
Autonomy needs boundaries
Sandboxing, least privilege, network controls and explicit approval limit the consequences when the agent is wrong.