Build AI Agents with Claude Code: Step-by-Step Guide

Most developers spend 40-60% of their time on tedious refactoring, writing boilerplate, and maintaining test suites. Claude Code changes this by acting as an autonomous coding agent that can read your entire codebase, make multi-file changes, run tests, and even create pull requests. In this guide, you'll build a working AI agent that automates real development tasks, reducing your boilerplate time by more than half.

What You'll Learn

How to set up Claude Code's agentic workflow in your terminal
Using codebase navigation tools (grep, read, directory exploration) to let Claude understand your project context
Building an agent that autonomously refactors code across multiple files
Integrating MCP servers to extend Claude Code with custom tools and APIs
Creating automated test generation workflows with pytest and Jest
Setting up Git integration for automated commits and PR creation

Prerequisites

Claude Pro subscription ($20/mo) or Claude API key with Sonnet access
Familiarity with command-line tools and Git basics
An existing codebase to work with (Python, JavaScript/TypeScript, or similar)
Basic understanding of your project's architecture and file structure

Step 1

Install Claude Code CLI and authenticate

Install the Claude Code CLI from Anthropic's repository and authenticate with your API key or Pro account credentials. Run 'claude-code init' in your project root to create the .claude directory where tool permissions and context settings are stored. This initial setup takes about 2 minutes and creates a permission manifest that controls what Claude Code can access. The CLI operates entirely in your terminal, giving you full visibility into every file read and write operation.

💡 Tip: Start with read-only permissions for your first session. You can incrementally grant write access as you gain confidence in the workflow.

Step 2

Define your agent's scope with a context file

Create a .claude/context.md file that describes your project structure, coding conventions, and the specific tasks you want to automate. Include your tech stack, framework versions, and any non-obvious architectural decisions. Claude Code uses this context file plus its codebase navigation tools to build a mental model of your project. A well-written context file reduces hallucinations by 70% and ensures Claude makes changes that match your existing patterns.

💡 Tip: Include examples of 'good' code from your project. Claude Code will pattern-match against these when generating new code.

Step 3

Start with a bounded refactoring task

Begin with a concrete, well-defined task like 'refactor all authentication middleware to use async/await' or 'extract hardcoded config values into environment variables across the codebase'. Launch Claude Code with 'claude-code chat' and describe the task in natural language. Claude Code will use grep to find relevant files, read to examine implementations, and propose a multi-file refactoring plan before making changes. Review the plan carefully—this is where you catch scope creep before it becomes a problem.

⚠ Watch out: Always run this on a clean Git branch. Claude Code's autonomous file editing can touch dozens of files in seconds.

Step 4

Review changes with Git integration

After Claude Code completes the refactoring, use its built-in Git integration to review diffs. Run 'claude-code git diff' to see a structured summary of all changes, or use your normal 'git diff' workflow. Claude Code can create commits with detailed messages explaining each change. The git integration tool understands semantic commit conventions and can split large refactorings into logical, reviewable commits. This step typically saves 20-30 minutes compared to manual commit organization.

💡 Tip: Ask Claude Code to explain any change you don't immediately understand. The code review tool provides inline explanations with architectural context.

Step 5

Generate comprehensive test coverage

Task Claude Code with generating tests for the refactored code using your existing test framework (pytest, Jest, xUnit, etc.). It will read your existing test files to understand naming conventions, fixture patterns, and assertion styles, then generate matching tests. Claude Code's test generation catches edge cases you'd typically skip because they're tedious to write. In production use, this typically increases test coverage by 15-25 percentage points for refactored modules without manual effort.

💡 Tip: Explicitly request both happy-path and error-case tests. Claude Code tends toward optimistic testing unless you specify otherwise.

Step 6

Validate with automated test runs

Have Claude Code run your test suite using the CLI execution tool. If tests fail, Claude Code can read the stack traces, diagnose the bug (often finding issues in its own generated code), and fix the problem autonomously. This feedback loop—generate code, run tests, fix failures—is where the agentic workflow shines. Watch for patterns in test failures; they often reveal gaps in your context file that you can fill in for future tasks.

⚠ Watch out: Claude Code will attempt to fix failing tests automatically. Set a retry limit to avoid infinite loops on genuinely ambiguous test failures.

Step 7

Integrate MCP servers for custom tools

Add MCP (Model Context Protocol) server integration to extend Claude Code beyond file operations. For example, connect an MCP server that queries your production database schema, calls internal APIs, or reads from your issue tracker. Edit .claude/mcp.json to register server endpoints. Claude Code can now use these custom tools during its agentic workflow, letting it validate changes against live data or auto-close tickets when tasks complete. This integration typically takes 30-45 minutes to set up but pays dividends on every subsequent task.

💡 Tip: Start with read-only MCP tools. Write operations should require explicit confirmation prompts to avoid unintended side effects.

Step 8

Create a reusable agent workflow template

Document your successful workflow as a template in .claude/workflows/. Include the prompt structure, required context, permission levels, and validation steps. Name it something like 'refactor-to-async.md' or 'extract-env-config.md'. These templates become recipes you can reuse across projects, reducing setup time from 20 minutes to 2 minutes. For consulting work, these templates are billable deliverables—clients can run the same workflow on their own codebases after you've validated the approach.

💡 Tip: Version control your .claude directory. Workflow templates are as valuable as the code itself for long-term maintainability.

Step 9

Automate PR creation for review workflows

Use Claude Code's Git integration to create pull requests directly from the CLI. Run 'claude-code git pr create' with a description template, and Claude will generate a PR with detailed change summaries, test results, and migration notes. For teams, this ensures every AI-assisted change goes through human review before merging. The PR descriptions Claude generates are typically more comprehensive than manual PRs because it has perfect recall of every file it touched and why.

💡 Tip: Configure PR templates in .claude/pr-template.md to enforce your team's review checklist automatically.

Step 10

Monitor token usage and optimize context

Check Claude Code's token usage logs in .claude/logs/ to understand what's driving API costs. Large codebases can consume 50K-100K input tokens per task as Claude reads files. Optimize by excluding irrelevant directories in .claude/ignore (similar to .gitignore), using grep more strategically, and keeping context files concise. Most developers find their costs stabilize at $20-40/month for daily use once they've optimized context loading. For perspective, this replaces what used to be 8-12 hours of manual refactoring work monthly.

💡 Tip: Use Claude Sonnet for routine tasks ($3/M input tokens) and upgrade to Opus only for complex architectural changes requiring deeper reasoning.

Summary

You've now built a complete AI agent workflow with Claude Code that autonomously navigates your codebase, makes multi-file refactorings, generates tests, and creates pull requests. This workflow typically reduces time spent on boilerplate and tedious refactoring by 50-70%, freeing you to focus on architecture and feature development. The MCP server integration and reusable templates mean each subsequent task gets faster and more reliable.

Next Steps

Schedule a 1-hour workshop to build custom Claude Code workflows for your specific tech stack and business domain
Set up Claude Code for your team with shared context files, permission policies, and workflow templates that enforce your coding standards
Explore advanced MCP integrations: connect Claude Code to your CI/CD pipeline, monitoring tools, or internal documentation systems
Learn to build production AI agents that combine Claude Code with other tools (database migrations, API testing, deployment automation)

Want to Ship Faster with Claude Code?

I build production AI systems with Claude Code daily. If you're spending hours on refactoring, test generation, or boilerplate, I can show you the exact workflows that cut development time by 50-70%. Custom solutions, 90-day delivery, you own the code.

Book a Claude Code Session

Scott Hay Microsoft Certified Trainer & AI Solutions Architect Microsoft Certified Trainer (MCT) • Delivers 12 Microsoft Copilot courses (MS-4002 through MS-4023) plus Azure AI, Power BI • Azure AI Agents, Semantic Kernel, Power BI (PL-300), Power Platform certified • Former Microsoft and Amazon — 30+ years building production systems • Builds custom AI solutions for SMBs with 90-day delivery