This course contains the use of artificial intelligence.

Modern enterprise software development demands more than generating code from a prompt. Large repositories contain multiple applications, shared packages, service boundaries, legacy components, security requirements, validation workflows, ownership rules, and organizational knowledge that an AI coding assistant must understand before making safe changes.

Claude Code for Enterprise Software Development teaches you how to transform Claude Code from a general coding assistant into a structured, repeatable, and governed AI coding harness for real engineering teams. Throughout the course, you will work with an enterprise commerce platform and progressively build the context, controls, workflows, and integrations Claude Code needs to operate effectively inside a complex codebase.

You will begin with the fundamentals of Claude Code, repository navigation, prompting, and codebase exploration. You will then learn how to reduce context noise by creating a practical CODEBASE_MAPmd, defining global project rules with CLAUDEmd, and adding layered service-level instruction files. These techniques help Claude understand architecture, ownership, dependencies, commands, boundaries, and expectations before changing code.

The course goes beyond prompt engineering. You will design clear validation commands, establish a measurable definition of done, and require Claude to report test, lint, type-check, and build results honestly. You will also configure safer tool usage by protecting secrets, excluding generated files, reviewing terminal commands, and applying least-privilege permissions.

Every major concept is reinforced through hands-on implementation. You will install and configure Claude Code, map a multi-service repository, write global and nested instruction files, define validation workflows, create permission rules, automate checks with hooks, build reusable engineering skills, configure code intelligence, develop an internal MCP server, delegate investigation to subagents, and package the completed harness for team adoption. Rather than studying isolated features, you will see how each component works together as part of a production-oriented engineering system designed for consistency, traceability, maintainability, and controlled execution.

Next, you will automate engineering workflows using Claude Code hooks, package repeatable procedures as Agent Skills, and improve large-codebase navigation through code intelligence and language-server capabilities. You will connect Claude Code to internal architecture, ownership, and engineering knowledge through the Model Context Protocol (MCP) while applying appropriate security and access controls.

For more advanced workflows, you will create specialized subagents for read-only investigation, security review, testing analysis, and structured reporting. You will then package CLAUDEmd files, skills, hooks, MCP integrations, and review practices into reusable Claude Code plugins that can be versioned, tested, distributed, and maintained across teams.

The final sections focus on enterprise AI governance, adoption strategy, human review, privacy, data handling, effectiveness metrics, incident tracking, configuration ownership, and organization-wide rollout. In the capstone, you will use the completed harness to implement a refund feature and compare Claude Code’s performance before and after the enterprise controls are introduced.

This practical, project-based course is designed for developers, architects, platform engineers, technical leads, and engineering managers who want to use AI-assisted software development responsibly at scale. By the end, you will have a complete blueprint for creating a safer, more accurate, and more productive Claude Code environment for large repositories and enterprise engineering teams.