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AI Coding Agent Guides

Use these guides to compare Codex, Claude Code, Cursor, and GitHub Copilot; choose repository instruction files; design repeatable agent loops; and secure MCP access before giving agents more autonomy.

Guide overview

KyenAI guides help software teams make practical decisions about AI coding agents. Start with the four-tool comparison hub when choosing among Codex, Claude Code, Cursor, and GitHub Copilot. Use the instruction-file support matrix when the problem is repository guidance. Use the loop engineering guide when agent work needs verification commands, retry limits, and stop rules. Use the MCP security and governance guides before adding tool access, secrets, logs, approvals, or revocation. Each guide gives the answer first, then adds source notes, decision tables, checklists, and next steps for implementation.

Guide tracks

Follow one track when you want a focused reading path from first decision to implementation details.

Instruction files

Start here when your team needs one clear policy for AGENTS.md, CLAUDE.md, Copilot instructions, and Cursor rules. The support matrix explains which file each tool surface reads, then the template and examples turn that decision into repo-ready guidance.

Beginner entry: CLAUDE.md vs Copilot Instructions vs AGENTS.mdAdvanced page: AGENTS.md Examples for Codex
Recommended reading order
  1. CLAUDE.md vs Copilot Instructions support matrixDecide which instruction file belongs to each tool before writing more rules.
  2. AGENTS.md template for coding agentsTurn the decision into a concise repo policy.
  3. AGENTS.md examples for Node.js, Python, and monoreposAdapt the template to real repo shapes.
  4. Does GitHub Copilot read CLAUDE.md?Resolve the Copilot-specific support question by surface.

Agent loops

Use this track when a one-shot prompt is no longer enough. The loop engineering guide defines the operating loop, then related guides show how subagents, hooks, agent mode, and cloud execution fit into a controlled workflow.

Beginner entry: Loop Engineering for AI Coding AgentsAdvanced page: Claude Code Subagents Workflow Examples
Recommended reading order
  1. What is loop engineering?Start with Addy Osmani context, coding-agent examples, stop rules, and token caps.
  2. Claude Code hooks and MCP setupAdd deterministic checks and tool access safely.
  3. Claude Code subagents workflow examplesSplit research, editing, and review into bounded roles.
  4. Agent mode vs chat mode in IDEDecide when the assistant should act instead of only answer.

MCP and security

Use this track before giving an AI coding agent access to tools, credentials, repositories, or production-adjacent systems. Start with MCP security, then connect it to governance rules for permissions, approvals, logs, and revocation.

Beginner entry: AI Coding Agent Governance ChecklistAdvanced page: Secure MCP Servers for AI Coding Agents
Recommended reading order
  1. Secure MCP servers for AI coding agentsThreat-model MCP access, credentials, logs, and revocation.
  2. AI coding agent governance checklistDefine permissions, approvals, audit logs, and owner review.

Comparisons and migrations

Use this track when the team needs to choose a tool, execution model, or migration path. The comparison guides focus on workflow fit, security boundaries, review effort, and same-repo evaluation rather than generic pros and cons.

Beginner entry: Local vs Cloud AI Coding AgentsAdvanced page: AI Coding Agents Comparison
Recommended reading order
  1. Claude Code alternatives by workflowChoose a replacement pilot by the constraint you actually need to change.
  2. AI coding agents comparisonBuild a Codex, Claude Code, GitHub Copilot, or Cursor shortlist by operating model.
  3. Local vs cloud AI coding agentChoose an execution model by security, cost, and speed.
  4. Codex vs Claude CodeCompare workflows with a controlled same-task protocol.
  5. Codex vs GitHub CopilotCompare OpenAI-native delegation with the GitHub control plane.
  6. Antigravity CLI migration from Gemini CLIHandle tool migration as commands, auth, and docs change.
Practical examples tutorial

Claude Code subagents workflow examples

Workflow patterns for using Claude Code subagents to split research, implementation, review, migration, and verification work.

Who it helps: Developers using Claude Code for larger codebases, migrations, and multi-step reviews.

Setup guide and control map

Claude Code Hooks vs MCP: Setup, Examples and Security

A setup-oriented guide for combining Claude Code hooks, skills, and MCP servers without creating brittle automation.

Who it helps: Developers and platform teams wiring Claude Code into repo-specific workflows.

MCP security checklist and threat model

MCP Server Security Checklist: 25 Controls for AI Agents

Audit MCP server security with 25 source-backed controls covering authentication, permissions, prompt injection, secrets, sandboxing, logging, revocation, and allow-or-deny tests.

Who it helps: Engineering leaders, security reviewers, platform teams, and developer tooling owners.

Interactive MCP troubleshooting guide

MCP Server Not Showing Tools? Diagnose tools/list in 8 Checks

Fix an MCP server that is missing, connected with zero tools, stale, filtered, or visible but never called across Claude Code, Cursor, GitHub Copilot, and other clients.

Who it helps: Developers, MCP server authors, platform teams, and AI coding tool administrators.

Migration guide and checklist

Antigravity CLI migration from Gemini CLI

A migration checklist for Gemini CLI users moving to Antigravity CLI, with the key dates, retained concepts, and verification steps.

Who it helps: Developers and teams using Gemini CLI or Google coding agents.

Comparison decision guide

Codex vs Claude Code

Compare Codex vs Claude Code by workflow shape, public same-task examples, repository instructions, review effort, and a same-repo checklist your team can run before standardizing.

Who it helps: Developers, staff engineers, and team leads comparing agentic coding tools for real repositories.

Template and checklist

AGENTS.md Template for Codex and Monorepos

Build, audit, copy, and download concise AGENTS.md starters for Codex, Node.js, Python, and monorepos with commands, boundaries, and review checks.

Who it helps: Developers setting up Codex-compatible agents, team leads standardizing repo instructions, and maintainers cleaning stale agent rules.

Support matrix

Does GitHub Copilot Read CLAUDE.md? Support Matrix by Surface

GitHub Copilot support for CLAUDE.md is surface-specific. Use the GitHub support matrix before relying on it, and keep .github/copilot-instructions.md as the broad Copilot baseline.

Who it helps: Maintainers standardizing Copilot, Claude Code, and Codex repository instruction files.

Examples and templates

AGENTS.md Examples for Codex: Node.js, Python and Monorepos

Copy practical AGENTS.md patterns for Codex projects, including Node.js apps, Python services, and monorepos with nested package instructions.

Who it helps: Developers and maintainers writing Codex repository instructions for real projects.

Decision tree

Agent Mode vs Chat Mode: Differences, Risks and When to Use Each

Use chat mode for questions and small explanations. Use agent mode when the assistant must inspect files, edit code, run commands, react to errors, and bring a task closer to done.

Who it helps: Developers trying AI-assisted IDE workflows without wanting a vendor-heavy feature comparison.

Decision matrix

Local vs Cloud AI Coding Agents: Security, Cost and Speed Compared

Use a local coding agent when privacy, local tools, and quick feedback matter most. Use a cloud coding agent when the task is long-running, isolated, or better handled as an asynchronous branch or pull request.

Who it helps: Engineering teams comparing local IDE or CLI agents with cloud sandboxes and asynchronous coding agents.

Governance checklist

AI Coding Agent Governance Checklist: Permissions, Logs and Approvals

A useful coding agent governance checklist covers identity, permissions, tool access, logs, human approval, sandboxing, cost limits, and incident response. Start with a small pilot before wider rollout.

Who it helps: Engineering managers, platform teams, security reviewers, and developer-experience teams rolling out coding agents.

Practical loop design guide

What Is Loop Engineering for AI Coding Agents?

Learn loop engineering for AI coding agents through Addy Osmani's workflow context, a budget calculator, stop rules, and a downloadable proof-of-done JSON contract.

Who it helps: Developers, staff engineers, and platform teams adopting agentic coding workflows in Cursor, Claude Code, Codex, or custom CI agents.

AI coding agents comparison hub

AI Coding Agents Comparison: Codex, Claude Code, Cursor, and Copilot

Compare Codex, Claude Code, Cursor, and GitHub Copilot by starting surface, repository instructions, parallel work, model boundary, permissions, review path, and team governance.

Who it helps: Developers, platform teams, engineering managers, security reviewers, and buyers choosing an AI coding-agent operating model.

Comparison decision guide

Codex vs GitHub Copilot: Which Coding Agent Fits Your Team?

Compare Codex vs GitHub Copilot by app, CLI, IDE, cloud-agent, instruction-file, model, parallel-work, permission, billing, and team-governance fit—with a six-question decision tool.

Who it helps: Developers, platform teams, engineering managers, and enterprise buyers evaluating coding-agent workflows.

Original data report

AI Coding Agent Instruction File Adoption Report — Q3 2026

Explore a reproducible public GitHub snapshot of 400 AGENTS.md, CLAUDE.md, Copilot instruction, and Cursor rule files, including indexed match counts, language mix, test commands, security rules, and common gaps.

Who it helps: Engineering leaders, developer-tool researchers, platform teams, and maintainers designing repository instructions for coding agents.

Alternatives decision guide

7 Claude Code Alternatives for Different Coding Workflows

Compare Codex, GitHub Copilot, Cursor, Gemini CLI, Cline, Aider, and OpenCode by the workflow problem you need to solve—not by an invented universal ranking.

Who it helps: Developers, staff engineers, platform teams, security reviewers, and buyers evaluating a replacement or complement for Claude Code.