Quick takeaways
- AI coding agents work best on small, verifiable tasks with clear acceptance criteria.
- Ask the agent to inspect first, change second, and verify last.
- Review changed files like you would review a junior developer's work.
AI coding assistants just leveled up
Fireship breaks down the latest jump in AI coding assistants and what it means for developers.
Best tasks for AI coding agents
- Reading a codebase and explaining how a feature works.
- Writing or improving tests for existing behavior.
- Fixing small bugs with clear reproduction steps.
- Refactoring isolated code without changing behavior.
- Updating documentation, examples, and developer notes.
- Building small features when acceptance criteria are clear.
How I would brief a coding agent
A good coding-agent brief has four parts: context, scope, constraints, and verification. Tell the agent what problem to solve, where to look, what not to change, and how to prove the result works. Do not start with "fix this repo" unless the repo is tiny.
Tasks to avoid at first
Avoid broad rewrites, ambiguous product decisions, security-sensitive changes, database migrations, payment flows, and large architectural shifts until you have a strong review process. Agents can help with these later, but they should not own them silently.
The biggest mistake I see with coding agents is giving them a task that is too broad, then accepting a large diff because it looks productive. Smaller tasks create better review, better tests, and less cleanup.
A good coding-agent prompt
Review checklist
- Read every changed file and understand why it changed.
- Run the relevant tests, linters, or build checks.
- Check edge cases, empty states, errors, and permissions.
- Reject unrelated refactors or formatting churn.
- Ask for a concise summary of behavior, risks, and verification.
Suggested workflow
Hands-on coding-agent exercise
- Pick a small project or sample repository.
- Ask the agent to explain the folder structure and identify test commands.
- Ask it to add one test for an existing function.
- Review the diff and run the test.
- Ask the agent to summarize what changed and what risk remains.
This exercise teaches the full loop: inspect, plan, implement, verify, and review. That loop matters more than any single coding-agent tool.
Tool-specific briefs
Different coding agents fit different workflows. Here is a quick guide to the most popular options.
Cursor guide
VS Code-style editor with Composer, Agent mode, and strong multi-file edits.
Read guideClaude Code guide
Terminal-first agent for repo exploration, tests, and natural-language commands.
Read guideGitHub Copilot guide
Deep editor integration for inline suggestions, chat, and PR reviews.
Read guideCursor vs Copilot
Head-to-head comparison for choosing your primary coding assistant.
Read comparisonAI instructions and .cursorrules
Most coding agents accept project-level instructions. A good instructions file sets context, constraints, and review expectations. Here is an example for a Cursor .cursorrules file:
# Project context
- This is a React + TypeScript frontend with a Node API.
- Use functional components and hooks. Prefer named exports.
- Run `npm run lint` and `npm run test` before finishing a task.
# Constraints
- Do not change unrelated files or refactor for style only.
- Never commit secrets or hardcoded API keys.
- Ask for clarification if a requirement is ambiguous.
# Verification
- Add or update tests for changed behavior.
- Summarize changed files, risks, and verification steps.For founders, developers, and marketers
- Founders: Use coding agents for prototypes, internal tools, landing page changes, and technical exploration, but still get review before production.
- Developers: Use agents as pair programmers for investigation, test coverage, and small implementation tasks.
- Marketers: Use agents for website edits, tracking snippets, content page updates, and structured handoff notes for engineers.
Without AI vs. with AI
| Task | Without AI | With AI |
|---|---|---|
| Task scoping | Developers give a vague request and hope the output matches intent. | Agents break work into verifiable steps with acceptance criteria before writing code. |
| Repo context | Humans search files, tests, and docs to understand what to change. | Agents inspect the repository, list relevant files, and explain current behavior first. |
| Code changes | Engineers write every line by hand or accept large unreviewed diffs. | Agents generate small, scoped diffs that a human reviews like a junior developer's work. |
| Verification | Tests and linters are run inconsistently after a change. | Agents run checks automatically and report results with a concise evidence summary. |
| Review discipline | Big diffs get approved because they look productive. | Reviewers reject unrelated refactors and verify every changed file before merging. |
FAQ
Can coding agents replace code reviews?
No. They help produce changes, but human review remains important for architecture, product intent, security, and risk.
What is the best first task for a coding agent?
Ask it to explain a small part of a repo, add tests, or fix a well-scoped bug with clear acceptance criteria.
Should coding agents commit directly?
Only after you trust the workflow. Early on, review changes locally before committing or merging.
Can non-developers use AI coding agents?
Yes, but they should use them for prototypes, website edits, technical exploration, or handoff notes, and get developer review before production changes.
What should a coding-agent summary include?
It should include changed files, behavior changes, tests run, risks, and anything it could not verify.
Can a coding agent handle an entire feature end to end?
Only for small, well-specified features. Break larger work into milestones and review each one.
What makes a good coding-agent prompt?
Include context, scope, constraints, and verification: what to do, what not to touch, and how to prove it works.
How do I stop agents from rewriting unrelated code?
State constraints explicitly, review diffs carefully, and reject any change outside the requested scope.
Are AI coding agents safe for production code?
Yes if every change is reviewed, tested, and deployed through your normal pipeline; no if the agent merges on its own.
Which agent should a solo developer start with?
Start with one integrated into your editor that can see the repo, run tests, and produce small diffs for review.