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The Agentic Engineer: Mastering Claude Code for the SDLC

Course Summary

This course provides developers and engineers with the implementation strategies and best practices for building production-ready agentic systems using Claude Code. Participants will explore advanced context management, secure integration and real-world patterns for automating the software development lifecycle. The course focuses on moving beyond basic chat-based interactions to create efficient, reliable, and autonomous workflows. While introducing an agentic framework for organizational alignment, the course emphasizes hands-on mastery of Claude Code’s native features, such as custom Skills and Checkpoints.

Prerequisites:

To get the most out of this session, participants should have:

  • Strong experience and programming proficiency (e.g., Python, JavaScript)
  • Familiarity with command-line tools and Git workflows
  • Prior foundational generative AI experience using Claude (via API or Claude.ai) 
  • Basic understanding of prompt engineering

Purpose
Build production-ready agentic systems using Claude Code
Audience
Core Technology Teams seeking to integrate AI-powered coding assistants safely into global workflows
Role
Software Developers | AI/ML Engineers | Technical Managers
Skill level
Intermediate
Style
Lecture | Hands-on Activities | Labs
Duration
4 days
Related technologies
AI/ML | Generative AI | Git

 

Learning objectives
  • Set up Claude Code for daily task delegation and autonomous refactoring
  • Apply context-management strategies to handle large-scale applications without data leakage
  • Use MCP to securely connect agents to internal data and external APIs
  • Design reliable agentic patterns to handle complex work-like unit testing and legacy migrations
  • Apply production patterns for auditing AI-generated code and monitoring for security vulnerabilities

What you'll learn:

In this The Agentic Engineer: Mastering Claude Code for the SDLC course, you'll learn:

Claude Code Fundamentals

  • Key differences between Claude.ai and the Claude Code CLI
    • Executing commands in a terminal-based environment
    • The shift from Generative AI (chatbots) to Agentic AI (autonomous agents)
  • Configuring the CLI and managing permissions for local repository access
    • Accessing the file system and repo structure
    • Environment-specific configurations
  • Core commands for planning refactors, writing code, and running tests
    • Delegating "drudge work"
      • Updating legacy code
      • Writing unit tests autonomously
    • Leveraging the agent to fix its own errors
  • Using Checkpoints for experimentation and safe rollbacks of AI actions
    • Managing state snapshots during complex refactors
    • Creating a "fail-safe" environment

Context Management & Secure Integrations

  • Token management strategies for 200K windows in large codebases
    • Preventing hallucinations while staying within limits
    • Optimizing context for large-scale applications
  • Implementing selective inclusion and context filtering to protect PII
    • Using .claudeignore to avoid processing sensitive or restricted files
    • Aligning agentic deployment with security and privacy standards
  • Introduction to MCP
    • Using the Model Context Protocol to connect agents to internal systems
      • Standardizing how agents access internal data silos and legacy documentation
      • Custom MCP servers to bridge AI agents and enterprise tools
  • Building progressive disclosure patterns and tool-use strategies for developer agents
    • Designing "Skills" as a context optimization pattern
    • Structuring tool permissions

Agentic Workflows & Orchestration

  • Creating hierarchical subagent configurations for complex engineering tasks
    • Orchestrating specialized agents
    • Structuring .md subagent configuration files
  • Introduction to bMAD
    • Using the bMAD framework to structure personas
      • Defining "Analyst" and "Architect" personas
      • Applying bMAD principles
  • Passing requirements and managing memory between specialized agents
    • Maintaining state and context across multi-agent workflows for large features
    • Establishing hand-off patterns between human oversight and agentic execution
  • Implementing spec-driven development where humans audit AI-generated plans
    • Writing to high-level orchestration and plan review
    • Human-in-the-loop governance

Governance & Real-World Application

  • Auditing and reviewing agent-produced code for security vulnerabilities
    • Developing new soft skills for reviewing "code you didn't write"
    • Ensuring safe deployment by auditing agent output
  • CI/CD Integration
    • Strategies for embedding Claude Code into automated pipelines
      • Automating PR reviews and bug-fixing scripts
      • Integrating agentic verification steps into the deployment lifecycle
  • Monitoring & Reliability
    • Identifying and fixing common agentic failure modes
      • Establishing patterns for debugging agent loops or incorrect context retrieval
      • Utilizing monitoring patterns to track "AI ROI" and efficiency gains

The Agentic Engineer: Mastering Claude Code for the SDLC

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

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