AI coding agents compared

The coding agent question in 2026 is not whether to use one. It is which shape fits your work: terminal, editor, or cloud. The three shapes have different strengths, and most senior engineers ended up running two of them.

Terminal agents: the heavy machinery

You describe a task in prose, the agent reads the repo, edits files, runs tests, and iterates until green. Claude Code and OpenAI Codex are the reference points here, with Gemini CLI and open-source alternatives in the same shape.

Strengths: whole-repo context, multi-file edits, running the test loop themselves. This is where agentic behavior pays - the agent sees failures and fixes its own work.

Watch for: context management on large repos, cost on long sessions, and the failure mode where the agent confidently rewrites something it did not understand. Give it a bounded task and a test suite that tells the truth.

Editor agents: the flow-state layer

Cursor and GitHub Copilot (Windsurf was folded into Cognition's Devin Desktop in mid-2026): the agent lives where you type. You keep the keyboard, it predicts, patches, and answers questions anchored to your open files.

Strengths: low latency, tight loop, you keep judgment in the loop at every keystroke. Best for exploratory work, unfamiliar codebases you are learning, and precise edits where you know exactly what you want.

Watch for: the multi-file ceiling. Editor agents are stronger at completing your intent than pursuing theirs. When a task needs planning across the repo, hand it to a terminal agent.

Cloud agents: the overnight shift

Devin and similar: assign a ticket, walk away, review a pull request later. In 2026 these work best on bounded, mechanical, high-volume tasks - test backfills, API migrations, dependency upgrades across many repos.

Watch for: success rates fall sharply on ambiguous tickets. Give cloud agents work where the definition of done is mechanical, and treat the PR review as non-negotiable - these agents fail forward, shipping plausible mistakes with full confidence.

The honest comparison criteria

Ignore benchmark leaderboards; they shrink under real workloads. Evaluate on four things you can test yourself in a week:

Multi-file competence. Give each agent the same real refactor. Compare the diff, not the vibes.

Recovery behavior. Break a test and see whether the agent finds the actual cause or sprays edits until something passes. Recovery is where agents separate.

Context discipline. Does it read what matters and ignore the rest, or does it drown and start hallucinating imports? Large repos expose this fast.

Undo and audit. Can you see what it did and roll it back? Agents without audit trails are not junior developers, they are interns who delete their own history.

The working setup

Most teams landed on: an editor agent for daily flow, a terminal agent for anything spanning more than two files, and a cloud agent for the mechanical backlog. Review at commit boundaries, keep irreversible actions - deletes, force pushes, production - behind human hands, and log which agent touched what.

The uncomfortable summary: the best coding agent is whichever one you have disciplined checkpoints around. Autonomy without review is how confident wrong code ships at 3am - and the agent will feel great about it the entire time.

Maintained monthly; expect the names to reshuffle and the shapes to hold.