Techniques for getting more out of AI coding agents and large language models — using them well, not building them. The emphasis is practitioner-facing: how to make a model running in a loop produce correct work reliably and economically.

The section shares one running example: using Claude Code to write a page for this wiki. It is a real agentic workload over artifacts in this repository, so each topic below can point at the same concrete task instead of inventing a fresh hypothetical.

• Context engineering — managing what the model sees: the context budget, curation, compaction, sub-agent isolation, and the pitfalls that cause wrong actions.
  • Claude Code — those techniques applied end-to-end in a real harness; the section’s worked example.
• Prompt engineering — wording the instructions the model receives.
• Prompt caching & cost — the token economics behind latency and spend.
• Agentic workflows — planning, tool use, delegation, and verification loops.
• Agentic engineering — the lifecycle around the loop: evaluation, observability, guardrails, and cost engineering.
• AI plugins — bundled extensions to an agentic harness: what they are, when to install one, and when to author your own.
  • Using a Claude Code plugin — anatomy, install, context cost, and trust applied to Anthropic’s harness.
  • Authoring a plugin — why packaging pays off even with one user, and the minimum scaffold to get started.
• MCP — the open protocol underneath most plugin tool surfaces.