How do I get better at coding with AI agents?
Updated
To get better at coding with AI agents, practice on unfamiliar real codebases under a time limit, keep a record of your prompts and test runs, and review that record to change one habit at a time. Using an agent more does not make you better at directing it. Deliberate practice on the parts you control does: scoping the problem, directing the agent, verifying its output, recovering from failures, and explaining the result.
Typing speed and syntax recall matter less when an agent writes the first draft. What remains is judgment, and judgment shows up in decisions you can observe and improve.
Most developers are already in this position. In Stack Overflow's 2026 Developer Survey, 79% of respondents said they want to improve their AI skills, 66% of AI users work in coding agents, and 93% said they need source attribution to trust what an AI gives them. Checking the output is now part of the job.
The five skills to work on
These map to the moments where a round with an agent is usually won or lost. Each is something you can watch yourself do, which is what makes it practicable.
- Scoping: find the affected code, its callers and its tests before asking for a change. Reproduce the failure first.
- Directing: give the agent a bounded task with the command, the expected behavior and your constraints, not a pasted issue or a bare error.
- Verifying: run the targeted test and nearby regression tests, read the output, and review the diff for unrelated edits or weakened tests.
- Recovering: when an attempt fails, form a new hypothesis from the evidence instead of asking the agent to try again.
- Explaining: describe the cause, the fix, the checks and what remains unverified without leaning on the agent's summary.
A practice loop that builds them
Practice that changes behavior looks less like reading tips and more like rehearsal with review. One loop takes about an hour and works with any agent.
- Pick a task you have not solved in a codebase you do not know. Familiar problems measure memory, not judgment.
- Set a timer. A limit forces the tradeoffs real work and interviews force: when to stop exploring, when to verify, when to submit.
- Keep the record: prompts, commands, test output and the final diff. Without it you review what you remember, not what you did.
- Review one moment where a different action would have helped, and name the habit you will try next time.
- Repeat on a new task and check whether the habit actually changed in the new record.
Measure progress by behavior, not one score
Task difficulty varies, so a higher score on an easier task proves little. Compare specific behaviors across rounds instead: did you reproduce the failure before the first edit, did your prompts carry a hypothesis, did you run regression tests before submitting, did a failed attempt lead to a new idea or a repeat.
Track outcome and process separately. A passing patch reached by luck and a failing patch reached by sound reasoning teach different lessons, and both are worth reviewing.
Practicing with PraxisAI
PraxisAI runs this loop in the browser: timed bug-fix challenges from real open-source repositories, take-home projects built from a brief, and an AI agent in VS Code. Each round records prompts, agent activity and test evidence, and the report scores six dimensions: outcome, prompt quality, token economy, verification, speed and recovery.
Free practice shows a basic score, verdict and grading result. Detailed reports with prompt coaching and replay require active plan access. The scores come from rules and heuristics; they can point you at a habit worth changing but cannot predict job performance.