Guide

AI Coding Agents Explained: How They Work in 2026

A practical explainer of AI coding agents: what they are, how they differ from completions and chat, and which tools offer agent-style workflows.

By AI Coding Tools Directory•2026-02-28•10 min read
Last reviewed: 2026-02-28
ACTD
AI Coding Tools Directory

Editorial Team

The AI Coding Tools Directory editorial team researches and reviews AI-powered development tools to help developers find the best solutions for their workflows.

AI coding agents go beyond suggesting the next line: they plan, execute, and iterate on development tasks. This guide explains how they work and which tools deliver agent-style workflows.

Quick Answer

AI coding agents take high-level goals (e.g., "add a login flow" or "fix this bug") and autonomously plan and execute steps: reading code, editing files, running commands, and debugging. They require less hand-holding than inline completions or simple chat. Leading tools: Cursor (Agent mode), Claude Code, Windsurf (Cascade).

Agents vs Completions vs Chat

Mode What it does Human involvement
Inline completion Predicts next tokens/lines You accept or reject each suggestion
Chat Answers questions, suggests code You copy, edit, or apply manually
Agent Plans and executes multi-step tasks You approve file changes and commands

Agents still run under your oversight: you review diffs and approve commands before they take effect.

How Agents Work

Step What happens
Goal You describe a task: "Add a dark mode toggle to the header."
Planning The agent breaks it into steps (read files, identify components, plan edits).
Execution It edits files, runs commands, runs tests.
Iteration If something fails, it analyzes and retries.
Review You approve or reject changes before they are applied.

Tools With Agent Workflows

Cursor (Agent mode)

  • Plans and executes multi-file edits, runs tests, debugs.
  • Integrates with Composer and your codebase.
  • 25+ models; background agents on higher tiers.
  • Cursor

Claude Code

  • Terminal-first agent; edits files, runs tests, manages git.
  • Uses Claude models; Agent Teams for parallel work.
  • IDE extensions for VS Code and JetBrains.
  • Claude Code

Windsurf (Cascade)

  • Cascade agents for multi-step coding in the IDE.
  • Fast Context for codebase retrieval.
  • Unlimited inline completions on free tier.
  • Windsurf

Devin

  • Ticket-to-PR workflows (Slack, Linear, Jira).
  • Web IDE with shell and browser control.
  • Enterprise-oriented; contact for pricing.
  • Devin

OpenAI Codex

  • Cloud coding agent with parallel sandboxes.
  • Desktop app; integrates with development workflows.
  • OpenAI Codex

Best Practices for Using Agents

  • Start with small tasks — "Add a loading state to this component" before "refactor the auth system."
  • Review all changes — Inspect diffs before accepting; treat agent output like a PR.
  • Use permission models — Prefer tools that require approval for file edits and commands.
  • Provide context — Point to specific files, mention your stack, and describe constraints.
  • Iterate — If output is wrong, give targeted feedback rather than redoing the whole task.

When Agents Make Sense

Good fit Less ideal
Repetitive refactors One-off tiny edits
Multi-file features Simple single-line fixes
Debugging with many steps Highly sensitive or compliance-heavy code
Prototyping and exploration Production deploys without review

Final Takeaways

  1. Agents accelerate multi-step work by planning and executing tasks with your approval.
  2. Different styles: IDE-based (Cursor, Windsurf), terminal-first (Claude Code), ticket-driven (Devin).
  3. Always review changes before applying.

Related in This Cluster

Related guides: AI code generation | Directory

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Tools Mentioned in This Article

Claude Code

Anthropic's terminal-based AI coding agent with 80.9% SWE-bench, Agent Teams, and GitHub Actions

Subscription
→

Cursor

The AI-native code editor with $1B+ ARR, 25+ models, and background agents on dedicated VMs

Freemium
→

OpenAI Codex

Cloud coding agent with 1M+ developers, Desktop App, and parallel sandboxed environments

Freemium
→

Windsurf

AI-native IDE with Cascade agents and SWE model family

Paid
→

Workflow Resources

Cookbook

AI-Powered Code Review & Quality

Automate code review and enforce quality standards using AI-powered tools and agentic workflows.

Cookbook

Building AI-Powered Applications

Build applications powered by LLMs, RAG, and AI agents using Claude Code, Cursor, and modern AI frameworks.

Cookbook

Building APIs & Backends with AI Agents

Design and build robust APIs and backend services with AI coding agents, from REST to GraphQL.

Cookbook

Debugging with AI Agents

Systematically debug complex issues using AI coding agents with structured workflows and MCP integrations.

Skill

Change risk triage

A systematic method for categorizing AI-generated code changes by blast radius and required verification depth, preventing high-risk changes from shipping without adequate review.

Skill

Configuring MCP servers

A cross-tool guide to setting up Model Context Protocol servers in Cursor, Claude Code, Codex, and VS Code, including server types, authentication, and common patterns.

Skill

Local model quality loop

Improve code output quality when using local AI models by combining rules files, iterative retries with error feedback, and test-backed validation gates.

Skill

Plan-implement-verify loop

A structured execution pattern for safe AI-assisted coding changes that prevents scope creep and ensures every edit is backed by test evidence.

MCP Server

AWS MCP Server

Open source MCP servers from AWS Labs that give AI coding agents access to AWS documentation, best practices, and contextual guidance for building on AWS.

MCP Server

Docker MCP Server

Docker MCP Gateway orchestrates MCP servers in isolated containers, providing secure discovery and execution of Model Context Protocol servers across AI coding tools.

MCP Server

Figma MCP Server

Official Figma MCP server that brings design context, variables, components, and Code Connect data into AI coding sessions for design-to-code workflows.

MCP Server

Firebase MCP Server

Experimental Firebase MCP server that gives AI coding agents access to Firestore, Auth, security rules, Cloud Messaging, and project management through the Firebase CLI.

Frequently Asked Questions

What is an AI coding agent?
An AI coding agent is a system that can plan, execute, and iterate on coding tasks—editing files, running commands, and debugging—with less step-by-step guidance than traditional completions or chat.
How do agents differ from Copilot or inline completions?
Completions suggest the next token or snippet. Agents take higher-level goals (e.g., 'fix this bug') and execute multi-step plans: read code, edit files, run tests, fix errors. They act more autonomously.
Are AI coding agents safe to use?
Use tools that require approval for file changes and command execution. Cursor, Claude Code, and similar tools show diffs and ask before applying. Avoid running agents with broad write access without review.
What are the best AI coding agents?
Cursor (Agent mode), Claude Code, Windsurf (Cascade), Devin, and OpenAI Codex are leading options. Cursor and Windsurf are IDE-based; Claude Code is terminal-first; Devin targets ticket-to-PR workflows.
Can agents work on my entire codebase?
Yes, but context limits apply. Agents use indexing, retrieval, and context windows to understand repos. Large codebases may need chunked work or explicit file references.

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