What is agentic coding?
Agentic coding is a shift in how developers work with AI. Instead of the AI helping you write code line by line, you give it a high-level instruction and the AI agent plans, writes, tests, and fixes code on its own.
Think of it like the difference between having a junior developer who needs step-by-step guidance and a senior engineer you can task with a feature and trust them to figure out the details.
The agent uses a reason-act loop: it breaks down your request, executes the steps, verifies the output, and adjusts if something is wrong. This loop runs automatically until the task is done.
Agentic vs vibe coding
The two terms describe different workflows.
| Aspect | Vibe coding | Agentic coding |
|---|---|---|
| Role of AI | Real-time coding partner | Autonomous worker |
| Role of developer | Full control, writes most code | Gives direction, reviews output |
| Workflow | AI completes what you type | You describe what you want, AI executes |
| Best for | Quick drafts, boilerplate | Complex features, refactoring |
Vibe coding is like pair programming with an AI that finishes your sentences. Agentic coding is closer to delegating a task to a competent engineer.
Tools for agentic coding
Several tools now offer agentic capabilities:
- Claude Code: Strong at deep reasoning, handles complex multi-step tasks
- OpenAI Codex: Agent-first design, good at multitasking across files
- Gemini CLI: Free from Google (free tier available), getting better with each update
- Cursor: Standalone IDE with built-in agent mode
- GitHub Copilot: Agent mode available inside VS Code (free tier included)
- OpenCode: Open source, free to install (Zen has free models including DeepSeek V4 Flash, plus BYOK), over 184K GitHub stars
The right tool depends on your workflow and budget. Most offer free tiers or trial periods.
Benefits of agentic coding
- Focus on architecture. You describe the structure, the agent handles implementation.
- Complex workflows. One instruction can trigger multiple steps across files.
- Self-correcting. The agent reads errors, fixes them, and retries without asking.
- Automated testing. Integration with test runners means the agent can verify its own output.
Criticisms and tradeoffs
Agentic coding is not without controversy.
Junior developers may miss the learning that comes from writing code line by line. Reviewing AI-generated code teaches you less than writing it yourself. More than a few experienced developers on Hacker News have questioned whether agentic coding produces net-positive results in practice.
Agents also hallucinate. They can produce code that looks correct but has logical errors, security issues, or performance problems.
How to get started
- Start with a free tier tool like Gemini CLI, OpenCode Zen (DeepSeek V4 Flash is free), or Copilot Free
- Choose a small, well-scoped task as your first experiment
- Use end-to-end tests to verify the agent’s output
- Review everything the agent generates at first
- Develop your taste: the more you understand the domain, the better the agent performs
Agentic coding is still new. It is not a replacement for understanding how code works. But used well, it can handle the tedious parts of development so you can focus on the interesting ones.
This post is part of the @aicoding_id series on AI coding tools. Follow us on TikTok for more reviews and tutorials.