Course · 7 chapters
Coding Agents Landscape
Choose the right AI coding agent for your team. Compare Claude Code, OpenAI Codex, Gemini CLI, GitHub Copilot, and Cursor on architecture, permissions, and cost. 7 chapters, foundations.
What you'll be able to do
- Compare the top 5 coding agents
- Map terminal, cloud, and IDE agents
- Assess each agent's permissions model
- Compare coding agent costs
- Match an agent to your team
- Plan a multi-agent rollout
What's inside
- 1Coding Agents Landscape: Start Here
A 12-minute orientation to five coding agents whose feature lists have converged near 1M context — so the real decision is fit, not capability.
- 2Claude Code Overview
Anthropic's terminal-native coding agent — architecture, 1M-token context, permissions, cost model, and honest trade-offs for engineering managers evaluating their options.
- 3OpenAI Codex Overview
OpenAI's async-first coding agent — CLI and cloud task architecture, sandboxed execution, permissions model, cost structure, and honest trade-offs for engineering managers evaluating their options.
- 4Gemini CLI Overview
Google's open-source terminal agent — being sunset for consumer, Pro, and Ultra users on 18 June 2026 in favor of the closed-source Antigravity CLI.
- 5GitHub Copilot Overview
Microsoft/GitHub's IDE-native coding assistant ecosystem — surfaces, architecture, context handling, permissions, cost model, and honest trade-offs for engineering managers evaluating their options.
- 6Cursor Overview
The AI-native IDE that forks VS Code and weaves multiple models into every editing surface — architecture, context handling, permissions, cost model, and honest trade-offs for engineering managers evaluating their options.
- 7Picking the Right Agent
A decision framework for engineering managers evaluating coding agents — dimensions that matter, team archetypes, multi-agent strategies, and rollout playbook.
Frequently asked questions
- What will I learn in the Coding Agents Landscape course?
- You learn how five major AI coding agents (Claude Code, OpenAI Codex, Gemini CLI, GitHub Copilot, and Cursor) differ in architecture, context window, permissions, and cost. The final chapter gives you a decision framework to match an agent to your team and plan a rollout.
- Who is this coding agents course for?
- It is built for engineers and engineering managers who need to evaluate and choose AI coding agents for a team. It is a foundations-level survey, so it suits anyone making or influencing the tooling decision.
- Do I need to code or have experience to take this course?
- Some software engineering background helps, because the course covers architecture, sandboxed execution, and cost models. You do not write code during the course. It is an evaluation and comparison survey, not a hands-on build.
- How long is the course and is there a certificate?
- The course has 7 chapters and runs about 140 minutes (roughly 2 hours and 20 minutes). Completing the chapters earns a certificate you can share.
- Is the Coding Agents Landscape course free?
- No. It is a paid course included with an AI Academy by Anthropos subscription.
Earn a certificate
Complete all chapters to receive your certificate of completion.