AI Driven Development

Master the AI Agent Factory paradigm — from foundational concepts to enterprise-grade agent pipelines using Claude Code, MCP, and Spec-Driven Development

Course Overview

The AI Agent Factory program teaches AI-native software development through a paradigm shift: treating General Agents as thinking partners and Custom Agents as deployed solutions. You will learn to orchestrate AI tools, write clear specifications, and build production-ready pipelines — not just prompt ChatGPT.

Approach:Seven structured chapters with embedded "Try With AI" practical applications. Theory is grounded in immediate, domain-specific practice throughout.

What You'll Learn

Cite inflection point evidence with concrete data and articulate why AI changes development
Distinguish General from Custom Agents and choose the right approach
Explain and apply the Five Powers framework in real projects
Navigate and implement AIFF standards (MCP, AGENTS.md, Agent Skills)
Execute Spec-Driven Development workflows from requirements to deployment
Apply all seven problem-solving principles in agent task design
Design monetization strategies using the Digital FTE model
Sell AI-driven development solutions to enterprise customers
Build production-grade agent pipelines with Claude Code and Cowork

Who Should Attend

Software developers ready to lead in the AI-native era
Technical leads and engineering managers adopting AI workflows
Freelancers and consultants offering AI development services
Entrepreneurs building AI-powered products and automations
Recent CS graduates seeking competitive differentiation

Duration

10 Weeks

Chapters

7 Chapters

Format

Online / Live

Certificate

Available

Level

Intermediate+

Prerequisites

Basic coding

Course Rating

4.7 (30+ students)

Enroll via WhatsApp

Course Curriculum

Chapter 12: The AI Agent Factory Paradigm

Establish conceptual foundations and the business case for AI-native development.

Lessons 1–3: Foundational Concepts

Evidence of the 2025 AI inflection point (ICPC scores, 84% developer adoption)

Three core LLM operational constraints: statelessness, probabilistic outputs, context limits

OODA Loop evolution from coder to orchestrator role

Lessons 4–5: Technical Architecture

Five Powers enabling autonomous agents: See, Hear, Reason, Act, Remember

Three-layer AI development stack

AIFF standards ecosystem (MCP, AGENTS.md, Agent Skills)

Lessons 6–10: Business & Strategy

Digital FTE business model and monetization

Nine pillars of AI-Driven Development

Spec-Driven Development methodology

Enterprise sales strategies for the $100–400 billion market

Chapter 13: Markdown — Writing Instructions for AI

Master technical communication fundamentals required to direct AI agents effectively.

Core Topics

Headings, lists, and nested structures

Code blocks and inline code formatting

Links, images, and tables

Writing clear AGENTS.md and SKILL.md documentation

Best practices for AI-readable instruction files

Chapter 14: General Agents — Claude Code & Cowork

Set up and operate production-grade AI agents using Claude Code and Cowork workflows.

Setup & Configuration

Claude Code CLI installation and configuration

CLAUDE.md persistent project context

MCP server integration and compilation

Workflow & Automation

Claude Cowork desktop workflows with Chrome automation

Google Workspace, Notion, and Slack connectors

Building repeatable agent pipelines for real tasks

Chapter 15: Effective Context Engineering

Apply quality-control discipline to agent context for reliable, consistent outputs.

Core Topics

Context quality assessment framework

The U-shaped attention curve and why position matters

Position sensitivity research and practical implications

Multi-agent coordination and handoff patterns

Designing prompts that survive context compression

Chapter 16: Spec-Driven Development

Move from conversational 'vibe coding' to rigorous specifications-first methodology.

Core Topics

Why specs before code reduces rework and hallucination

Writing effective feature specifications for agents

Memory systems: persistent context across sessions

Task orchestration and dependency management

Review and validation workflows

Chapter 17: Seven Principles of General Agent Problem Solving

Internalize a principled framework for safe, reliable agent task execution.

The Seven Principles

Principle 1 — Bash is the Key: leverage the shell as a universal tool

Principle 2 — Code as Universal Interface

Principle 3 — Verification as Core Step

Principle 4 — Small Reversible Decomposition

Principle 5 — Persisting State in Files

Principle 6 — Constraints and Safety

Principle 7 — Observability: logging and traceability

Chapter 18: Claude Code for Teams, CI/CD & Advanced Configuration

Deploy AI-driven development practices across teams and production pipelines.

Team & Production

Team workflow patterns with shared CLAUDE.md

Continuous integration patterns with Claude Code

Production deployment strategies

Advanced MCP configurations for enterprise environments

Security, permissions, and audit trails

Learning Outcomes

Cite inflection point evidence with concrete data and articulate why AI changes development

Distinguish General from Custom Agents and choose the right approach

Explain and apply the Five Powers framework in real projects

Navigate and implement AIFF standards (MCP, AGENTS.md, Agent Skills)

Execute Spec-Driven Development workflows from requirements to deployment

Apply all seven problem-solving principles in agent task design

Design monetization strategies using the Digital FTE model

Sell AI-driven development solutions to enterprise customers

Build production-grade agent pipelines with Claude Code and Cowork

Lead the AI Development Revolution

The question isn't whether AI will change development — it's whether you'll lead or follow. Enroll now and master the AI Agent Factory methodology.

Contact via WhatsApp

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AI Driven Development

Master the AI Agent Factory paradigm — from foundational concepts to enterprise-grade agent pipelines using Claude Code, MCP, and Spec-Driven Development

Course Overview

Approach:Seven structured chapters with embedded "Try With AI" practical applications. Theory is grounded in immediate, domain-specific practice throughout.

What You'll Learn

Cite inflection point evidence with concrete data and articulate why AI changes development
Distinguish General from Custom Agents and choose the right approach
Explain and apply the Five Powers framework in real projects
Navigate and implement AIFF standards (MCP, AGENTS.md, Agent Skills)
Execute Spec-Driven Development workflows from requirements to deployment
Apply all seven problem-solving principles in agent task design
Design monetization strategies using the Digital FTE model
Sell AI-driven development solutions to enterprise customers
Build production-grade agent pipelines with Claude Code and Cowork

Who Should Attend

Software developers ready to lead in the AI-native era
Technical leads and engineering managers adopting AI workflows
Freelancers and consultants offering AI development services
Entrepreneurs building AI-powered products and automations
Recent CS graduates seeking competitive differentiation

Duration

10 Weeks

Chapters

7 Chapters

Format

Online / Live

Certificate

Available

Level

Intermediate+

Prerequisites

Basic coding

Course Rating

4.7 (30+ students)

Enroll via WhatsApp

Course Curriculum

Chapter 12: The AI Agent Factory Paradigm

Establish conceptual foundations and the business case for AI-native development.

Lessons 1–3: Foundational Concepts

Evidence of the 2025 AI inflection point (ICPC scores, 84% developer adoption)

Three core LLM operational constraints: statelessness, probabilistic outputs, context limits

OODA Loop evolution from coder to orchestrator role

Lessons 4–5: Technical Architecture

Five Powers enabling autonomous agents: See, Hear, Reason, Act, Remember

Three-layer AI development stack

AIFF standards ecosystem (MCP, AGENTS.md, Agent Skills)

Lessons 6–10: Business & Strategy

Digital FTE business model and monetization

Nine pillars of AI-Driven Development

Spec-Driven Development methodology

Enterprise sales strategies for the $100–400 billion market

Chapter 13: Markdown — Writing Instructions for AI

Master technical communication fundamentals required to direct AI agents effectively.

Core Topics

Headings, lists, and nested structures

Code blocks and inline code formatting

Links, images, and tables

Writing clear AGENTS.md and SKILL.md documentation

Best practices for AI-readable instruction files

Chapter 14: General Agents — Claude Code & Cowork

Set up and operate production-grade AI agents using Claude Code and Cowork workflows.

Setup & Configuration

Claude Code CLI installation and configuration

CLAUDE.md persistent project context

MCP server integration and compilation

Workflow & Automation

Claude Cowork desktop workflows with Chrome automation

Google Workspace, Notion, and Slack connectors

Building repeatable agent pipelines for real tasks

Chapter 15: Effective Context Engineering

Apply quality-control discipline to agent context for reliable, consistent outputs.

Core Topics

Context quality assessment framework

The U-shaped attention curve and why position matters

Position sensitivity research and practical implications

Multi-agent coordination and handoff patterns

Designing prompts that survive context compression

Chapter 16: Spec-Driven Development

Move from conversational 'vibe coding' to rigorous specifications-first methodology.

Core Topics

Why specs before code reduces rework and hallucination

Writing effective feature specifications for agents

Memory systems: persistent context across sessions

Task orchestration and dependency management

Review and validation workflows

Chapter 17: Seven Principles of General Agent Problem Solving

Internalize a principled framework for safe, reliable agent task execution.

The Seven Principles

Principle 1 — Bash is the Key: leverage the shell as a universal tool

Principle 2 — Code as Universal Interface

Principle 3 — Verification as Core Step

Principle 4 — Small Reversible Decomposition

Principle 5 — Persisting State in Files

Principle 6 — Constraints and Safety

Principle 7 — Observability: logging and traceability

Chapter 18: Claude Code for Teams, CI/CD & Advanced Configuration

Deploy AI-driven development practices across teams and production pipelines.

Team & Production

Team workflow patterns with shared CLAUDE.md

Continuous integration patterns with Claude Code

Production deployment strategies

Advanced MCP configurations for enterprise environments

Security, permissions, and audit trails

Learning Outcomes

Cite inflection point evidence with concrete data and articulate why AI changes development

Distinguish General from Custom Agents and choose the right approach

Explain and apply the Five Powers framework in real projects

Navigate and implement AIFF standards (MCP, AGENTS.md, Agent Skills)

Execute Spec-Driven Development workflows from requirements to deployment

Apply all seven problem-solving principles in agent task design

Design monetization strategies using the Digital FTE model

Sell AI-driven development solutions to enterprise customers

Build production-grade agent pipelines with Claude Code and Cowork

Lead the AI Development Revolution

The question isn't whether AI will change development — it's whether you'll lead or follow. Enroll now and master the AI Agent Factory methodology.

Contact via WhatsApp