Learn agentic coding by practicing the full work loop: define an outcome, provide context, inspect a plan, allow tools, verify results, and review the diff. Start with small repositories and reversible tasks. The key skill is supervising evidence-based execution, not memorizing clever prompts.
First ask an agent to explain a code path and cite the relevant files. Next, have it add a focused test or fix a reproducible bug in an isolated branch. Then try a small multi-file feature with written acceptance criteria. At each stage, specify allowed commands and require the agent to report changes, test results, failures, and uncertainty. Deliberately introduce a failing check so you can learn how the system recovers. Practice stopping a run, narrowing scope, and correcting a plan without discarding useful work.
Study repository basics alongside the agent: version control, tests, dependency management, logs, permissions, and deployment. Without these, you cannot judge whether a confident completion claim is true. Keep production data, secrets, and destructive access out of early exercises.
Maintain a small benchmark task and repeat it as your workflow improves. Review what context was missing and update project instructions. Once you can predict risk, read diffs, and verify outcomes, grant more autonomy one capability at a time. That progression teaches transferable agentic coding rather than dependence on one interface.
