Transform software engineering with AI-first workflows, coding agents, and scalable automation.

AI coding agents are changing how engineering teams build, maintain, and scale software. This immersive 3-day training equips teams with practical Claude Code workflows to accelerate development, automate repetitive tasks, improve code quality, and adopt AI-first engineering practices.

  • Why get trained: Learn to configure Claude Code, structure project context, generate and refactor code, automate workflows, use MCP servers, improve testing, and integrate AI with Git workflows.
  • Why it matters: Accelerate delivery, reduce repetitive engineering work, improve code quality, manage technical debt, and scale AI-assisted development safely.
  • Who should attend: Software Engineers, Engineering Managers, Tech Leads, Architects, Platform Engineering Teams, DevOps Engineers, Product Engineering Teams, Transformation & Innovation Teams, and Engineering Leaders.

Gain hands-on experience through real-world simulations, practical workshops, AI-assisted engineering workflows, and expert guidance. HRD Corp Claimable.

Overview

Software engineering is entering a new era in which development teams are increasingly moving from manually writing code to orchestrating AI-assisted engineering workflows. AI coding agents like Claude Code are changing how engineering firms design systems, manage large codebases, accelerate delivery, improve code quality, automate repetitive work, and scale productivity.

Sustainable productivity gains from AI tools are not casual. To maximize AI-assisted software engineering’s value, organizations need structured AI-first engineering practices, context-aware workflows, governance models, reusable automation patterns, and scalable development methods.

This immersive 3-day enterprise training helps engineering teams adopt practical, scalable agentic software engineering practices using Claude Code. Unlike traditional coding courses, this program focuses on engineering transformation, enabling teams to build repeatable, scalable, and enterprise-ready AI-assisted engineering systems.

By the end of the program, participants will understand how to integrate AI agents into real engineering workflows to improve delivery speed, reduce operational overhead, and modernize software engineering practices at scale.

Skills Covered

  • Understand how AI coding agents are changing software development
  • Differentiate between AI-assisted coding and AI-first engineering
  • Apply structured AI workflows in engineering teams
  • Install and configure Claude Code
  • Structure project context for better outcomes
  • Manage context windows and persistent instructions
  • Generate, modify, and refactor code using AI assistance
  • Automate repetitive engineering tasks
  • Create reusable engineering workflows
  • Use hooks and automation
  • Implement reusable custom commands
  • Work with MCP servers and multi-step workflows
  • Analyze code quality and technical debt
  • Improve test coverage using AI assistance
  • Apply clean code and refactoring practices
  •  Integrate Claude Code with Git workflows
  • Coordinate AI-assisted parallel development
  • Scale engineering workflows safely

Prerequisites

None

Target Audience

  • Software Engineers
  • Engineering Managers
  • Tech Leads
  • Architects
  • Platform Engineering Teams
  • DevOps Engineers
  • Product Engineering Teams
  • Transformation & Innovation Teams
  • Engineering Leadership Teams

Course Curriculum

Module 1: Introduction to Agentic Software Engineering

  • What is Claude Code?
  • AI-assisted coding vs. agentic engineering
  • Evolution of engineering workflows
  • Human + AI collaboration models
  • Engineering productivity transformation
  • AI-first engineering concepts
  • Explore → Plan → Code → Commit workflows
  • Engineering operating model changes

Module 2: Claude Code Setup & Project Configuration

  • Claude Code installation
  • Project configuration
  • Persistent project context
  • CLAUDE.md structure
  • Context hierarchy
  • User vs project configuration
  • Context windows
  • Managing engineering memory

Module 3: Context Engineering & Structured Prompting

  • Context engineering fundamentals
  • Context windows and token management
  • Context layering
  • Working memory vs persistent memory
  • Rules vs skills vs prompts
  • Scratchpads and summaries
  • Retrieval strategies
  • Session management
  • Context degradation
  • Compact and session optimization

Module 4: Prompt Engineering for Engineering Workflows

  • Structured prompting
  • Few-shot engineering prompts
  • Code review prompts
  • Debugging prompts
  • Refactoring prompts
  • Test generation prompts
  • Architecture prompts
  • Workflow prompting
  • Reducing false positives
  • Structured outputs

Module 5: Agentic Architecture & Multi-Agent Systems

  • Custom slash commands
  • Skills and reusable workflows
  • Skill isolation
  • Workflow packaging
  • Team engineering standards
  • Reusable engineering playbooks
  • Engineering workflow standardization

Module 6: Custom Commands, Skills & Reusable Engineering Workflows

  • Custom slash commands
  • Skills and reusable workflows
  • Skill isolation
  • Workflow packaging
  • Team engineering standards
  • Reusable engineering playbooks
  • Engineering workflow standardization

Module 7: Hooks, Automation & MCP Servers

  • Hooks and automation
  • PreToolUse vs PostToolUse
  • Tool interception
  • Event-driven engineering workflows
  • MCP fundamentals
  • MCP tools and resources
  • MCP server integration
  • Shared engineering services
  • Tool governance

Module 8: Plugins, Superpowers & Extensible Engineering Systems

  • Claude Code plugin architecture
  • Plugin ecosystem overview
  • Plugin vs Skill vs Hook vs MCP vs Command
  • Installing and managing plugins
  • Superpowers methodology overview
  • Reusable engineering systems
  • Internal plugin ecosystems

Module 9: Code Quality, Testing & Technical Debt Modernization

  • Technical debt analysis
  • Code smell analysis
  • AI-assisted refactoring
  • Test generation
  • Review workflows
  • Multi-pass review systems
  • Independent verification workflows
  • Architecture modernization
  • Legacy system exploration

Module 10: AI-Integrated GitHub & CI/CD Workflows

  • AI-assisted PR reviews
  • CI/CD integration
  • Structured JSON outputs
  • Automated review systems
  • Batch workflows
  • AI quality gates
  • Delivery pipeline orchestration
  • GitHub integration patterns
  • AI-assisted release workflows

Module 11: Enterprise Reliability, Governance & Safety

  • AI engineering guardrails
  • Approval workflows
  • Human review systems
  • Validation and retry loops
  • Confidence calibration
  • Escalation patterns
  • Reliability engineering
  • Secure AI workflow design
  • Plugin governance

Module 12: Enterprise Capstone: Agentic Engineering Transformation

  • Analyze a real engineering workflow
  • Design AI-assisted delivery systems
  • Configure CLAUDE.md
  • Implement commands and hooks
  • Integrate MCP services
  • Apply GitHub workflows
  • Improve engineering quality
  • Create reusable automation
  • Present an engineering transformation roadmap

Dates & Locations

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Exam & Certification

Upon successful completion of the Agentic Fluency for Everyone with Claude training, participants will receive a certification from Agilemania.

Training & Certification Guide

Why train with Trainocate

This training helps professionals use Claude effectively for productivity, workflow execution, communication, reporting, analysis, and daily business tasks using practical AI-assisted workflows.

No. This is a business-focused training designed for non-technical as well as technical professionals.

Yes. The training covers how AI-assisted workflows can be applied to large and complex codebases using structured context management and scalable engineering practices.

Yes. Participants learn how AI-assisted workflows can automate repetitive engineering activities, reduce manual coordination, and improve delivery efficiency.

Software engineering is rapidly shifting toward AI-assisted execution. Teams that adopt structured AI-first engineering practices early will have a significant advantage in speed, scalability, and delivery efficiency.

Yes. The training is valuable for engineering leaders, architects, and tech decision-makers who want to build scalable AI-first engineering systems within their organizations.

Speak to a Training Consultant

All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631

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