This guide shows exactly how to build an Agentic AI coding assistant that works in your editor and terminal—using Claude Code in VS Code with a CLAUDE.md file, GitHub Copilot’s agent mode for scaffolding, Gemini CLI for long-context tasks, and CodeRabbit for automated PR reviews. Follow the steps and success checks to prove each part works before you scale it.

You can build an Agentic AI coding assistant that actually improves day-to-day work by wiring three layers: IDE edits with Claude Code and GitHub Copilot, terminal planning and batch operations with Gemini CLI, and automated PR reviews with CodeRabbit. I reviewed and analysed recent hands-on tutorials and found a repeatable pattern: instruction files like CLAUDE.md, agent or plan modes, and CI-friendly checks make agentic workflows reliable.

Our team also noted the broader shift toward the Model Context Protocol (MCP). Tools like Runway are surfacing features via MCP so assistants can call them from coding environments. This trend reinforces setting explicit tools, guardrails, and memory so your agents act with context and constraints.

What Is an Agentic AI Coding Assistant?

An Agentic AI coding assistant is a goal-driven helper that plans steps and takes actions—editing files, running tools, and proposing diffs—across your editor, terminal, and repo instead of only answering chat prompts. In practice, it combines instruction files, agent or plan modes, tool access such as MCP, and verification through tests and pull request reviews.

Who Is This For and When Should You Use It?

This setup fits US-based software engineers, indie developers, and technical teams who want reproducible workflows for REST APIs, unit tests, and CI-ready pull requests. Use it when tasks span multiple files or require iterative reasoning, and you want proof through tests, diffs, and automated reviews—not just chat outputs.

How Do You Set Up Claude Code with CLAUDE.md?

How to Set Up Claude Code as a Beginner

Claude Code in Visual Studio Code provides plan and agent modes that act on files with repository context. I reviewed and analysed examples showing that adding a CLAUDE.md file makes outputs more consistent and reduces back-and-forth.

Minimal CLAUDE.md template

Try it

• Plan mode prompt: “Plan changes to implement GET /products/:id with input validation and Jest tests. Show file-by-file steps.”
• Agent mode prompt: “Apply the approved plan. Propose diffs before writing. Then run npm test.”

How Do You Use Gemini CLI for Long-Context Terminal Tasks and Diffs?

Gemini CLI complements IDE agents by handling very long inputs and batch workflows in terminal. In our review, Gemini CLI shines when you paste multi-file specifications, large logs, or want iterative diffs against a patch goal.

Setup and workflow

1. Install Gemini CLI and authenticate.
2. Create a context folder with your specification, logs, and code excerpts.
3. Ask Gemini to plan and produce a patch, then apply it with git.

find src -name "*.ts" | xargs wc -l > context/size.txt

gemini plan \
--goal "Harden input validation and add unit tests for product controller" \
--context context/spec.md context/size.txt src/controllers/product.ts

gemini diff --target src/controllers/product.ts --tests test/product.spec.ts > patch.diff

git apply --3way patch.diff
npm test

Tips

• Feed failing test output back into Gemini CLI to refine the patch.
• Keep each iteration scoped to one module to reduce merge risk.

How Do You Scaffold and Refactor with Copilot’s Agent Mode?

GitHub Copilot’s agent mode is effective for scaffolding new components, routes, and basic tests quickly, then handing off deeper reasoning to Claude Code or Gemini CLI.

1. In a clean feature branch, ask Copilot agent to scaffold a products router, controller, and Jest tests.
2. Review the plan it proposes and confirm or edit the steps.
3. Let it generate boilerplate files and a starter test suite.
4. Switch to Claude Code or Gemini CLI for stricter validation logic or large refactors.

Verification tips

• Immediately run tests and lint after scaffolding.
• Ask Copilot to add JSDoc comments and example requests in README.

How Do You Automate Pull Request Reviews with CodeRabbit?

CodeRabbit provides AI-driven pull request reviews in GitHub and a CLI for local checks. Use it to enforce standards, surface risky diffs, and ask follow-up questions directly in the pull request.

Basic YAML configuration

Place .coderabbit.yaml in the repository root:

review:
  objectives:
    - Ensure new endpoints have unit tests and input validation.
    - Flag functions over 40 lines or missing JSDoc.
  severity:
    style: low
    testing: high
    security: high
  block_on:
    - missing_tests
    - introduced_dependency
  guidance:
    coding_style: "ESLint Standard, TypeScript strict"
    test_framework: "Jest"

cli:
  enabled: true
  commands:
    - npm ci
    - npm run lint
    - npm test

Local dry run before pushing a pull request

npx coderabbit review --local --branch feature/products-id

GitHub integration

• Install the CodeRabbit GitHub app for the repository or organization.
• Open a pull request; CodeRabbit posts review comments, summaries, and fix suggestions.
• Reply in-thread to iterate, then rerun checks.

What Does OpenClaw Add as an Open-Source Option?

OpenClaw is a customizable, locally hosted assistant you can script for editor or system actions. Use it when you want open-source control, offline modes, or bespoke tools that vendor plugins do not expose.

Practical uses

• Run cron jobs to regenerate API docs from JSDoc.
• Batch-run contract tests across services overnight.
• Trigger local scripts when a pull request label is added.

How Do You Add Guardrails, Avoid Pitfalls, and Wire MCP Tools?

Guardrails keep agents safe and useful. From our internal checklists and recent examples, the most reliable setups define constraints in code and configuration, not just prompts.

Checklist

• CLAUDE.md: Define constraints, allowed commands, and the definition of done.
• Tests: Ensure coverage gates run in CI.
• CodeRabbit: Block on missing tests or risky dependencies.
• Git: Use feature branches and small pull requests; avoid multi-module sweeps.
• MCP: Define tool interfaces the agent can call, such as a local test runner or documentation search.

Why MCP matters

Our team observed vendors exposing capabilities through MCP so assistants in VS Code or terminals can call tools natively. For developers, this means fewer context switches and more reliable actions.

Common mistakes

• Letting the agent install packages without review.
• Using vague goals in CLAUDE.md with no tests or definition of done.
• Running huge refactors in one pull request, making reviews noisy.

Which Tool Should Your Agent Use for Each Stage?

Stage	Recommended tools	Why
Scaffolding	Copilot agent mode	Fast file generation
Focused edits	Claude Code plan or agent mode	Clear plans and guarded diffs
Long-context	Gemini CLI	Handles large specifications and logs
Pull request review	CodeRabbit	Enforces tests and style
Local automation	OpenClaw	Scriptable, open-source control

Can You See AI Agents Examples That Prove It Works?

Yes. Here are compact AI Agents examples you can reproduce:

• REST API: Add GET /products/:id with validation and Jest tests, using Claude Code plan mode and then agent mode.
• Refactor: Use Gemini CLI to propose a diff that extracts validation into a utility and updates tests.
• Pull request review: Push the branch and let CodeRabbit block merge until tests exist and functions stay under 40 lines.

How Do You Build an Agentic AI Coding Assistant From Scratch End to End?

If you prefer a single flow for how to build AI Agents from scratch, use this checklist:

1. Repository hygiene: Add tests, ESLint, and CI basics.
2. Claude Code: Install it, write CLAUDE.md, and verify plan mode on a tiny task.
3. Copilot: Scaffold boilerplate modules and starter tests in a feature branch.
4. Gemini CLI: Iterate on long-context patches and apply diffs.
5. CodeRabbit: Add YAML, run the local CLI, then integrate with GitHub for pull request reviews.
6. MCP tools: Expose a documentation search or test runner so agents can call them explicitly.

Verification tips

• After each step, run npm test and create a small pull request to validate signal quality.
• Keep a CHANGELOG noting which steps are agent-generated versus human-edited.

What is an Agentic AI coding assistant in simple terms?

An Agentic AI coding assistant is a goal-driven helper that plans steps and takes actions across your codebase. Instead of only chatting, it edits files, proposes diffs, runs tests, and prepares pull requests using modes like plan and agent, guided by instruction files and verification checks.

How do I start if I want to know how to build AI Agents from scratch?

Start by establishing verification first: add tests, linting, and a CI run. Then install Claude Code, write a concise CLAUDE.md with goals and constraints, and validate plan mode on a tiny task. Add Copilot for scaffolding, Gemini CLI for long context, and CodeRabbit for pull request reviews.

What belongs in a good CLAUDE.md for plan or agent mode?

Include project purpose, coding style, allowed tools or commands, constraints such as function length or dependency rules, and a clear definition of done with tests. Add examples of acceptable diffs and require that agents propose changes before applying them when the risk is high.

When should I use Gemini CLI instead of my IDE agent?

Use Gemini CLI when you need to process long inputs, aggregate logs, or coordinate multi-file diffs. It excels at planning from large specifications and iterating on patches you can apply with git. Keep IDE agents for focused edits and quick refactors within a few files.

How does CodeRabbit improve pull request reviews?

CodeRabbit encodes review rules in YAML and applies them consistently. It flags missing tests, risky new dependencies, oversized functions, and style issues. You can run checks locally through the CLI before pushing and get threaded comments in GitHub pull requests for faster iteration.

Can I integrate tools through the Model Context Protocol?

Yes. MCP lets your assistant call predefined tools such as a test runner, documentation search, or local scripts. Defining tool interfaces improves reliability because the agent invokes explicit capabilities rather than guessing shell commands from prompts alone.

What are some AI Agents examples I can try today?

Try three quick ones: add a GET endpoint with validation and tests using Claude Code; use Gemini CLI to produce and apply a small refactor patch; then open a pull request and let CodeRabbit block merge until tests exist and style constraints pass. Each example has clear success criteria.

How do I keep agents from installing random dependencies?

Declare a dependency policy in CLAUDE.md and CodeRabbit YAML. Block merges if new packages appear without approval. Review plans in plan mode before agent mode applies changes, and keep each pull request small so dependency diffs are obvious.

Is GitHub Copilot agent mode enough on its own?

It is strong for scaffolding and simple refactors but may struggle with long-context reasoning or multi-file policy enforcement. Pair it with Claude Code for structured plans and Gemini CLI for large-context diffs, then use CodeRabbit to enforce tests and style in pull requests.

How do I prove my agentic workflow is working reliably?

Use objective checks after every stage: passing unit tests, clean diffs limited to intended files, and a green CodeRabbit review. Track these in CI, and keep pull requests small so each agentic change is reviewable and attributed with clear commit messages.