---
title: "AI Coding Agents: What They Are and How to Use Them"
date: "2026-06-14"
author: "Graham"
description: "A plain-English guide to AI coding agents: Claude Code, Codex, Gemini CLI, local vs cloud workflows, code review, tests, safety, and best practices."
tags: ["Developers", "Power of AI"]
url: "https://powerofai.ca/ai-coding-agents"
readTime: "7 min"
---

# AI Coding Agents: What They Are and How to Use Them

An AI coding agent is not just a chatbot that writes snippets. It can inspect a repo, edit files, run commands, use tools, and keep working through a goal.

That changes software work. The question is no longer "can AI write code?" The useful question is "which parts of the development loop can I safely hand to an agent?"

## Quick picks

- **Best for local pair-programming: Claude Code or Codex CLI.** Use a local agent when you want tight feedback, local tests, and direct control over the workspace.
- **Best for branch review: Codex review workflows.** Use an agent as a reviewer when the task is to find regressions, missing tests, and risky assumptions.
- **Best for unfamiliar repos: Read-only exploration first.** Agent value starts with understanding the codebase before editing it.
- **Best rule: Never skip verification.** The agent should run checks or clearly state what it could not verify.

## The agent loop

Most coding agents follow the same loop: read context, make a plan, edit files, run commands, inspect failures, and repeat. The difference between a good session and a messy one is how well that loop is constrained.

The human still owns the intent, judgment, and final approval. The agent handles the heavy reading, typing, searching, and first-pass debugging.

## What agents are good at

Agents shine on work with visible feedback: tests, builds, type checks, linting, screenshots, route checks, and diffs. The tighter the feedback loop, the better the result.

They are also useful for repo archaeology. A good agent can trace behavior across files faster than a human can click around.

- Bug fixes with a reproducible failure.
- Small feature work with existing patterns.
- Test writing and missing-case discovery.
- Code review and risk scanning.
- Documentation, changelog, and migration notes.
- Refactors with a narrow target and strong tests.

## Local vs cloud coding agents

Local agents are best when you need to steer closely, inspect UI, run local services, or work with files that are not pushed. Cloud agents are best when the task is well-scoped and can come back as a branch, review, or patch.

The future is not one agent. It is choosing the right agent surface for the job.

## Related Power of AI pages

- [Claude Code Best Practices](/claude-code-best-practices): A practical Claude Code workflow.
- [Codex Best Practices](/codex-best-practices): A practical Codex workflow.
- [Claude Code vs Codex](/claude-code-vs-codex): Choose the right agent.
- [What Is an AI Agent?](/what-is-an-ai-agent): Understand agents outside coding too.

## Sources and official references

- [OpenAI Codex](https://developers.openai.com/codex)
- [Anthropic Claude Code overview](https://docs.anthropic.com/en/docs/claude-code/overview)