Coding with Agents

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Level 3: Become a Power User ​

Last updated: 2026-08-05

The difference between using a coding agent and mastering one is like the difference between driving a car and running a fleet.

Chapter 2 took the machine apart: how agents work, how much autonomy to give them, and how Claude Code's memory, extension, and integration layers fit together. This chapter is where that anatomy becomes engineering practice — the systems and disciplines that separate a developer who gets 2x leverage from one who gets 10x.

Four chapters make up this level. One is written; the other three are outlined below and not yet in the navigation. Progress is tracked in ROADMAP.md.

  1. The orchestration layer — how to design the layer above individual agents: routing work between sessions, agents, and workflows; deciding what runs autonomously versus interactively; and composing sub-agents, hooks, and skills into repeatable pipelines instead of one-off tricks.

  2. Engineering frameworks — the reusable structures that make agent-assisted development reliable: project scaffolding, verification loops, task decomposition templates, and the frameworks emerging around agent-native engineering.

  3. Spec coding — specification-first development in depth: writing specs that agents can execute against, the six-dimension requirements system, and the delivery loop from spec to verified change.

  4. Working with Brownfield Codebases — power-user strategies for pointing agents at large existing systems: the three kinds of debt brownfield work accrues, the three layers of control (comprehension, constraint, verification), and the characterization test → seam → incremental refactor workflow that keeps agent-driven changes reversible.

Where you've been: Chapter 2 gave you the full anatomy — mental model, autonomy framework, collaboration modes, and the complete Claude Code toolkit from context management to systematic composition.

What's next: Individual mastery is powerful, but real impact comes from scaling these practices across a team. Chapter 4 tackles the hard problems of team development with agents: shared context, code review at AI speed, and testing strategies that keep quality high when output volume explodes.