You’ll learn: the mindset shift, how to size tasks for delegation, when to step in, and how to match autonomy to risk.
Mindset
Traditional AI assistance: you write code, AI suggests completions or answers questions. Agentic engineering: you define the task, the agent executes, you review the output. Your role shifts from implementer to architect and reviewer:- Defining clear tasks — enough context for autonomous execution
- Setting up guardrails — CLAUDE.md, hooks, tests that keep the agent in bounds
- Reviewing output — with the rigor you’d apply to a junior developer’s PR
Delegation Levels
Match autonomy to risk. Start at Draft until you trust the agent’s output in your codebase.| Level | You do | Agent does | Example |
|---|---|---|---|
| Suggest | Decide + implement | Analyze and recommend | ”What’s the best caching approach here?” |
| Draft | Review + approve | Plan and implement | ”Add pagination to the users endpoint.” |
| Execute | Verify after | Plan, implement, and verify | ”Fix this failing test. All tests must pass.” |
| Autonomous | Spot check | Full cycle including PR | Background agents, CI agents |
Task Sizing
Not every task is a good fit for delegation. Good agent tasks are:- Well-defined — clear success criteria the agent can verify
- Scoped — fits within one context window without losing important details
- Testable — has tests or a verifiable output
- Reversible — if it goes wrong, you can undo it (commit before starting)
Context
The agent’s effectiveness is bounded by what it knows. You control that through three levers:CLAUDE.md
Project-level context that persists across every session — commands, architecture, conventions, warnings. See the CLAUDE.md guide.Skills
Domain knowledge that loads when relevant. Instead of explaining your testing philosophy every session, encode it in a skill. See the skills stack for how to get started.Conversation
What you tell Claude in the current session. Be specific. Include error messages, file paths, expected vs. actual behavior. The agent can’t read your mind, but it can read your codebase — point it in the right direction.When to Intervene
| Let it run | Step in |
|---|---|
| Making steady progress toward the goal | Going in circles (same failed approach) |
| Changes are small, incremental, and testable | Making changes you didn’t ask for |
| You can verify the output | Approach is fundamentally wrong |
| About to do something irreversible |
Brownfield
Most real work is brownfield — existing codebases with history, tech debt, and implicit knowledge.- A good CLAUDE.md — the agent needs your conventions, gotchas, and architecture
- Tests — the safety net that lets the agent make changes confidently
- Incremental changes — small PRs that are easy to review, not massive rewrites
- Git discipline — commit before delegating, branch for risky changes, review diffs carefully
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