AI-Augmented Developer

Why Choose Garranto Academy for Your AI-Augmented Developer Training?

Garranto Academy provides expert-led sessions, real-world projects, and guided cognitive sprints that simulate true AI-human collaboration. Our program equips developers with practical, future-ready skills to build high-impact AI-driven software.

Course Overview:

The AI-Augmented Developer Program is an intensive, hands-on 3-day training designed to equip developers, engineers, and solution builders with the skills to co-create intelligent software using AI. This program teaches participants how to orchestrate AI copilots, automate architecture design, accelerate testing, and streamline deployment to deliver production-ready, intelligent solutions. Through immersive cognitive sprints, learners experience real-time AI-human collaboration, enabling them to shift from traditional coding practices to advanced, AI-driven development workflows. By integrating reasoning, adaptation, and automation, participants learn to build smarter, faster, and highly scalable systems. By the end of the program, developers will be prepared to leverage AI as a powerful engineering partner, enhancing productivity and innovation across software projects.

What You’ll Learn in Our AI-Augmented Developer Program?

Course Objectives:

Upon successful completion of this course, learners will be able to:

• ✓Collaborate effectively with multiple AI copilots for software design and development.

• ✓Generate, validate, and document end-to-end software architectures using AI tools.

• ✓Automate testing, CI/CD workflows, and deployment with minimal manual effort.

• ✓Use AI reasoning for debugging, performance optimization, and continuous refactoring.

• ✓Build and deliver an AI-generated MVP with complete documentation and traceability.

• ✓Demonstrate full-cycle AI-augmented engineering capability through the Proof of Intelligence challenge.

• ✓Strengthen productivity and innovation by integrating AI throughout the development lifecycle.

Prerequisites

• ✓Basic programming and version control knowledge

• ✓Good technical English communication skills

• ✓Interest in AI-driven software development

Course Outlines:

Sprint 1: The AI-Augmented Engineering Mindset

• ✓Objective: Transition from manual coding to AI-assisted co-engineering.

• ✓Key Topics:

• ✓Multi-AI workspace setup (Copilot, Cursor, Claude, ChatGPT, Devin)

• ✓Prompt patterns for modular coding and reasoning

• ✓AI-based version control and context management.

• ✓Practical Exercise: Rebuild with Reasoning – Re-implement an existing repository using multiple AI copilots.

• ✓Outcome: Mastery of collaborative AI-driven coding.

Sprint 2: AI-Generated Architecture and System Design

• ✓Objective: Design and validate enterprise-grade architectures using AI.

• ✓Key Topics:

• ✓UML, API, and data model generation via AI tools

• ✓Scalability validation and dependency analysis

• ✓C4-style system decomposition

• ✓Exercise: Blueprint Generation Challenge – Create and validate an AI-generated architecture.

• ✓Outcome: Complete architecture blueprint backed by AI reasoning.

Sprint 3: Collaborative Co-Development Workflows

• ✓Objective: Build software collaboratively through orchestrated AI copilots.

• ✓Key Topics:

• ✓AI-assisted modular development

• ✓Reasoning continuity and documentation automation

• ✓Governance and version management

• ✓Exercise: Three-Hour Cognitive Build – Develop a RESTful API and dashboard UI through AI copilots.

• ✓Outcome: Working full-stack application with collaboration trace report.

Sprint 4: Cognitive QA and AI-Driven DevOps

• ✓Objective: Automate testing and deployment pipelines using AI orchestration.

• ✓Key Topics:

• ✓AI-generated unit, integration, and load tests

• ✓CI/CD setup with GitHub Actions, Docker, Terraform

• ✓Automated release note generation

• ✓Exercise: AI Ops Pipeline – Create a fully automated testing and deployment workflow.

• ✓Outcome: AI-generated DevOps framework ready for deployment.

Sprint 5: Optimization, Debugging, and Continuous Improvement

• ✓Objective: Use AI for intelligent debugging and performance optimization

• ✓Key Topics:

• ✓AI-assisted bug detection and code refactoring

• ✓Log analytics and system tuning

• ✓Continuous feedback integration

• ✓Exercise: Refactor with Cognition – Optimize a legacy repository through AI-guided analysis.

• ✓Outcome: Explainable, optimized codebase demonstrating continuous improvement.

Sprint 6: Proof of Intelligence (PoI) – Build-to-Deploy Challenge

• ✓Objective: Integrate all modules to complete an AI-augmented full-cycle build.

• ✓Key Topics:

• ✓End-to-end AI collaboration for architecture, coding, testing, and deployment

• ✓Real-time reasoning trace and explainability

• ✓Capstone Project: Build, test, and deploy a live AI-augmented application within 24 hours.

• ✓Deliverables:

• ✓Hosted MVP + GitHub Repository

• ✓AI-Generated Documentation Suite

• ✓Collaboration and Reasoning Trace Report

• ✓Outcome: Verified Proof of Intelligence — full-cycle AI-augmented development capability.

Course Outcomes:

Upon completing the "AI-AUGMENTED DEVELOPER" course, participants will:

• ✓Collaborate seamlessly with multiple AI copilots across design, development, and optimization tasks.

• ✓Generate, validate, and document complete software architectures using advanced AI tools.

• ✓Automate testing, CI/CD pipelines, and deployment workflows with minimal manual effort.

• ✓Apply AI-driven reasoning for debugging, performance enhancement, and continuous code refactoring.

• ✓Build and deliver a functional AI-generated MVP with full documentation and traceability.

• ✓Demonstrate end-to-end AI-augmented engineering skills through the Proof of Intelligence challenge.

• ✓Enhance development speed, accuracy, and scalability using AI-powered workflows.

• ✓Transition from manual coding to efficient AI-assisted software engineering practices.

Key Benefits of Becoming an AI-Augmented Developer

Becoming an AI-Augmented Developer enables you to accelerate software development with AI copilots and automation. It boosts productivity, reduces manual effort, and empowers you to deliver smarter, more scalable solutions.

How AI Can Transform Your Software Engineering Workflow

AI transforms engineering workflows by automating repetitive tasks, optimizing performance, and enabling reasoning-driven development. It helps teams deliver faster, improve code quality, and innovate with advanced AI-assisted decision-making.