Agentic Coding with Claude Code training course

The Shifting Role of the Developer

• From code writer to analyst, agent architect, and reviewer
• From autocomplete and chat to agentic coding
• The agentic loop: reason, use tools, observe, iterate
• Agentic coding tool landscape: Claude Code, GitHub Copilot, Cursor, and OpenAI Codex

Introduction to Claude Code and LLMs

• Understanding Large Language Models and static vs. dynamic knowledge
• Training data cut-offs and the context window
• Where Claude Code runs: terminal CLI, IDE extensions, desktop, and web
• Model selection: choosing between Opus, Sonnet, and Haiku
• Plans, pricing, and cost-control habits
• Privacy and security considerations
• Lab: Installing and authenticating Claude Code and a first run

Fundamentals of Claude Code

• Keybindings and the slash command palette
• Built-in tools and feeding context
• Resetting and compacting context
• Checkpointing and rewinding conversations
• The permission model
• Lab: Complete a guided first task

AI Across the SDLC: Analysis and Design

• Scoping a task and extracting requirements with Claude
• Letting Claude interview to author a self-contained spec
• Asking questions to onboard to unfamiliar code
• Plan mode and extended thinking
• Reviewing and editing the plan before execution
• Lab: Designing the e-commerce platform — specs and plan

Integrations with MCP and CLI Tools

• What MCP is and how it fits the agentic loop
• Connecting and scoping MCP servers in Claude Code
• Common MCP servers: GitHub, databases, Figma, and browser control
• MCP server security considerations
• Tool search and keeping MCP context cost low
• The Skill plus CLI tools pattern as an efficient alternative
• Lab: Create a modern UI using Claude Code and the Playwright CLI

Skills, Agents, Dynamic Workflows and Hooks

• Project memory and instructions with CLAUDE.md
• Defining reusable knowledge and workflows with Skills
• Using pre-built skills, plugins, and marketplaces
• Subagents and custom agents for isolated contexts
• Parallel workflows with Git worktrees and agent teams
• Automating clean-up and guardrails with Hooks
• Orchestrating agents at scale with dynamic workflows
• When to use CLAUDE.md vs. Skills vs. Agents vs. Hooks vs. Workflows
• Lab: Building custom CLAUDE.md, Skills, Subagents, and Hooks

Automating Claude Code in CI/CD

• Headless mode: running Claude Code non-interactively and output formats
• Authenticating in CI
• Controlling tools and permissions non-interactively
• GitHub Actions: @claude mentions, issue-to-PR, and automated PR review
• Running Claude Code in Azure Pipelines
• Use cases: review, triage, documentation, and scheduled jobs
• Lab: Build a pipeline that runs Claude Code

AI Across the SDLC: Implementation and Testing

• Executing the plan with Claude Code
• Auto mode and permission allowlists
• Sandboxing for unattended runs
• Using test suites as guardrails for the agent
• The test-driven development loop with Claude
• Verification signals: tests, builds, linters, and screenshots
• Gating completion with /goal conditions and a Stop hook
• Lab: Building and testing the e-commerce platform with Claude Code

AI Across the SDLC: Reviewing and Verifying AI-Generated Code

• Reviewing large AI-produced diffs efficiently
• Static analysis and security scanning as objective review signals
• Verifying behaviour with tests, Playwright, and benchmarking tools
• Steering the agent to produce reviewable, incremental changes
• Lab: Reviewing, verifying, and hardening the e-commerce platform

Building Custom Applications with the Claude Agent SDK

• The Agent SDK as a TypeScript and Python library
• The agentic core and execution loop
• Configuring agent runs and authenticating
• Giving the agent custom tools
• Connecting external MCP servers programmatically
• Controlling autonomy
• Use cases: batch automation, CI/CD, and building your own agentic apps
• Lab: Build a small custom agent with the Agent SDK