Claude Code Training for Teams

What "agentic" means in practice, and how Claude Code differs from the completion tools a team may already have running. It shows what the tool does well. It also marks what still goes back to a person.

• What 'agentic' means: reading files and running commands, including multi-file edits
• How Claude Code runs in the terminal and inside your IDE
• Where it fits inside your team's existing development workflow
• What it does well, and where a human is still required
• First low-risk tasks that build intuition before anything critical

Getting Claude Code installed and configured the same way for every engineer. Then walking one task from a first prompt through to a reviewed, merged change, so the team leaves with a loop rather than ten personal habits.

• Installing and configuring Claude Code the same way across a team
• Scoping a task the agent can plausibly finish in one pass
• Walking the prompt-to-change loop end to end on your own repository
• Working in small, reviewable steps rather than one large change
• Connecting scripts via MCP
• Keeping a running record of prompts and setups that worked

Output quality tracks the context an agent is given more than almost anything else. This module builds a CLAUDE.md around your team's conventions and boundaries, then practices sizing that context correctly for a task on your own codebase.

• Writing a CLAUDE.md that states conventions and boundaries, including guardrails
• How project memory carries your standards from one session to the next
• Giving enough context for a task without burying the agent in files
• Pointing the agent at the right files and docs, with prior examples
• Prompting patterns for code
• Starting a shared library of prompts and project instructions

Speed without a review habit is risk on a faster clock, so this module builds the practice of reading, testing, and accepting agent-written changes.

• Reading a diff with the same skepticism you'd give a stranger's pull request
• Test-first, test-alongside patterns
• Recognizing the failure modes specific to generated code
• Keeping changes small enough that a review is honest
• Fitting agent output into the code review and CI you already run
• Knowing when to accept or redirect, and when to start over

What an agent may read or change, what it must never see, and how source code and customer data stay inside policy, KVKK included. A working part of the daily loop.

• Deciding what an agent may read and run, plus what it may change
• Permission and approval steps sized to the task at hand
• Keeping secrets and credentials out of every prompt, along with customer data
• Source-code confidentiality, and where that data is allowed to go
• KVKK-aware handling of personal data in AI-assisted development
• Writing an AI coding policy engineers will follow without being reminded

Moving from a handful of enthusiasts to a team using Claude Code safely and consistently. A pilot path sets the direction. The team picks the norms worth standardizing and uses metrics honest enough to separate adoption from mere activity.

• Choosing a pilot cohort and one workflow with measurable value
• A champions approach
• Deciding what to standardize as a norm and what to leave to judgment
• The one number that separates real adoption from raw activity
• A rollout plan shaped entirely around your own codebase