I'm a programmer trying to understand the current landscape of agentic coding. I've watched several introductory videos and currently have a Google AI Pro subscription, so I'm considering Gemini CLI as my starting point.
I'd appreciate practical advice from people who have actually used these tools:
1. Which CLI tools do you recommend for accessing coding models? Is Gemini CLI a reasonable choice with a Google AI Pro subscription?
2. When is a CLI workflow better than using an IDE integration such as Cursor, Windsurf, Cline, or GitHub Copilot?
3. Do separate agents for planning, architecture, programming, debugging, and testing provide real value, or is one general coding assistant usually enough?
4. Are frameworks such as CrewAI or LangGraph worth learning for a beginner, or should I start with simple instruction files such as AGENTS.md? Where can I find good examples of coding guidelines and system prompts?
5. What belongs in a project-specific agent configuration versus a global one? For example, should a general programmer or debugger agent be global while project conventions remain local?
I'd also appreciate recommendations for useful tutorials, videos, repositories, and examples of how you organize your own coding environment.
5 Answers
The fastest way to learn is to build something small rather than spending many more hours watching demonstrations. Start with a straightforward editor or CLI workflow, keep the model’s context focused, and review every change yourself. Frameworks such as CrewAI or LangGraph make more sense after you have a concrete need for multi-step orchestration, state management, or multiple specialized models.
Keep global instructions limited to preferences that apply everywhere, such as how you want explanations or commits formatted. Put project-specific commands, architecture, dependency choices, naming conventions, and test procedures in the repository. A local configuration can later become a reusable global one if you discover that the same guidance applies across many projects.
I’d start much simpler than building a collection of custom agents. A project-level AGENTS.md file is often enough to describe the stack, commands, coding conventions, testing requirements, and rules the assistant should always follow. You can add more focused markdown files in directories that contain complicated subsystems. Keep the context targeted instead of letting the tool scan the entire repository every time.
You probably don’t need separate planner, architect, programmer, and debugger agents at first. Use one assistant with clear project instructions and create a specialized prompt only when you repeatedly encounter a genuinely niche task. Many so-called agents are just reusable instructions, and adding orchestration frameworks introduces extra complexity before you know whether you need it.
IDE integrations are usually more convenient for everyday work because they already understand the files you have open and make it easy to review changes. A CLI becomes especially useful when you want repeatable prompts, scripts, automation, or a workflow that doesn’t depend on an editor. Try Gemini CLI since you already have access, but compare it with another tool before committing to one.
That makes sense. For a general AGENTS.md file, should I include broad rules such as asking for a plan first, being verbose, acting like a senior developer, and reviewing the changes afterward?