What Are AI Coding Agents?

AI coding agents are AI tools that take a task, such as "add rate limiting to the login endpoint", and carry it out on their own. A coding agent reads the codebase, edits files, runs commands and tests, and often opens a pull request (PR) for a person to review.

A chat assistant suggests code. An AI coding agent does the work across several steps and checks its own progress as it goes.

Most developers now use some form of AI coding tool, so the hard part has moved from writing code to checking it. The rest of this page covers how agents work, where they go wrong and how teams review their work.

What Changed in AI Coding Agents in 2026

In 2026, coding agents moved from trial to daily use, and checking their output became the slowest step.

Agents became routine. Of developers surveyed, 65.9% use AI coding assistants or coding agents, and 73% of those use them daily (October 6, 2026, Stack Overflow).

A few agents lead. Among developers who used a coding agent or assistant in the past year, 65.5% used Claude Code and 58.7% used GitHub Copilot (October 6, 2026, Stack Overflow).

Agent rules are becoming shared standards. The Linux Foundation’s Agentic AI Foundation now hosts 10 open projects, including AGENTS.md, MCP and Agent Skills, so the same rule files can work across different agents (October 22, 2026, Linux Foundation).

Trust stays conditional. 48% of developers trust AI output only when they can easily verify it, and 6.6% trust it with important work decisions (October 6, 2026, Stack Overflow).

Review became the bottleneck. Reviewing and validating AI-generated code was the main delivery constraint for 25.8% of developers and 26.0% of leaders (September 23, 2026, Qodo’s State of AI Code Quality report).

With agents writing more of the code, the review after the agent matters more than the agent itself.

How AI Coding Agents Work

Most coding agents run the same loop:

1. Plan. The agent reads the task and the relevant files, then decides on steps.
2. Act. The agent edits code, runs commands and calls tools, often through the Model Context Protocol (MCP), using servers such as the best MCP servers for codebase context.
3. Check. The agent runs tests, linters or a build and reads the results.
4. Repeat. The agent fixes what failed until the task looks done.
5. Hand off. The agent opens a PR or returns a diff for a person to review.

Agents run in three main places: inside the editor, in the terminal and in the cloud, where an agent works on a branch and opens a PR by itself.

AI Coding Agents vs AI Coding Assistants

The two terms overlap, and vendors use them loosely.

Where AI Coding Agents Go Wrong

Agents usually write code that compiles and passes the tests they wrote. The failures sit elsewhere:

• Logic that misses intent. The change does what the agent understood, not what the ticket asked. See logic errors.
• Security gaps. Missing authorization checks and unvalidated input look like working code. See secure code review.
• Broken neighbors. A change can break a caller in another service or repository that the agent never opened. See blast radius.
• Ignored team rules. Agents follow the files they’re given, such as AGENTS.md, but not the conventions that live in people’s heads. See AGENTS.md files.
• Self-checks that share blind spots. An agent that tests its own work tests what it thought of.

How to Review Code From AI Coding Agents

Teams that review agent output well tend to follow a few habits:

• Review every agent PR. Treat an agent as a fast new contributor, not as a trusted senior.
• Use a separate reviewer. A second tool with its own context catches what the writer missed.
• Review before the PR, too. A local review lets the agent fix issues before anyone else reads the diff. See pre-PR and local code review.
• Write the rules down. Rules in files can be checked on every PR. See coding agent rules.
• Keep humans on the merge. People decide what ships, with findings to help them.

Qodo’s Take: An Agent’s Work Needs a Reviewer That Didn’t Write It

Coding agents are good at producing a lot of plausible code quickly. Plausible isn’t the same as correct, and an agent’s own checks only cover what it already thought of.

You shouldn’t review code with the same tool that wrote it. A separate reviewer brings its own context, its own rules and no stake in the change being right.

Choose Qodo if your teams use several coding agents and need one review standard for all of them. Qodo reviews pull requests in GitHub, GitLab, Bitbucket, Azure DevOps and Gerrit, whichever agent wrote them.

Qodo is the best AI code review platform for teams using AI coding agents because it reviews agent PRs with full codebase context and the team’s own rules, and can review the agent’s changes locally before a PR opens.

Qodo Reviews PRs That Agents Open

By default, Qodo skips PRs opened by bot accounts, including some AI coding agents. Setting ignore_bot_pr = false for your Git provider turns on review for those PRs, and ignore_pr_authors keeps chosen bots excluded (last checked October 2026, Qodo’s docs). A team member can also comment /agentic_review to review any bot PR on demand.

Qodo Checks Changes Before the PR Exists

The Agentic Toolbox brings Qodo’s review into the agent’s session through a CLI (qodo review), MCP and plugins for Claude Code, Codex and Kiro (last checked October 2026, Qodo’s docs).

The Reviewer skill (Beta) reviews local changes with the same engine Qodo uses on PRs. After a PR opens, the Review Resolver skill pulls Qodo’s findings into the agent session so the agent can fix them.

Qodo Applies the Team’s Rules to Agent Code

Once Rule enforcement is on (Beta, on GitHub, GitLab, Bitbucket Cloud and Azure DevOps), Qodo checks each PR against rules imported from files such as AGENTS.md and CLAUDE.md and rules written in the portal (last checked October 2026, Qodo’s docs).

Rule Miner (Beta, on GitHub, GitLab and Azure DevOps) can also suggest rules from past PRs.

Findings are advisory, and your branch rules decide what merges. For a wider view of reviewers built for agent output, see the best agentic AI code reviewers for GitHub and GitLab.

Qodo Example: The Agent PR Nobody Had to Rewrite

Context: an organization like a logistics software company with about 200 engineers is using a cloud coding agent to pick up small tickets and open PRs from a bot account.

Problem: the agent’s PRs passed CI, but reviewers kept finding the same issues late: a missing tenant check, a retry loop without a timeout and helpers that duplicated an existing utility. Agent PRs also weren’t getting an AI review, because Qodo skips PRs from bot accounts by default.

How Qodo helped: the platform lead turned on review for bot PRs with ignore_bot_pr = false for the Git provider, imported the team’s AGENTS.md as rules and added the Reviewer skill to the agent’s workflow, so the agent runs a local review before it opens a PR.

Outcome: every agent PR now gets a Qodo review, and rule findings show up in the PR before merge. The agent also sees findings from its local review before the PR exists.

Ways to Review Code From AI Coding Agents

The table compares common ways teams check agent output.

Qodo features in this table were checked against Qodo’s documentation as of October 28, 2026.