AI First Engineering Native Agentic Software Delivery – Live Training
(Master AI First Engineering, Prompt Engineering, Agentic Workflows, Spec Driven Development, AI-Assisted SDLC, MCP, AI Testing & AI-Native Development with Real-Time Projects and Practical Industry Workflows)
AI First Engineering is a practical training program designed for software professionals, QA engineers, business analysts, DevOps engineers, tech leads, engineering managers, and final-year technical students who want to understand how modern software delivery is evolving with Generative AI, AI coding agents, reusable skills, agentic workflows, and spec-driven development.
This course goes beyond basic prompt engineering. It teaches learners how to redesign the software development lifecycle using AI-first practices such as structured specifications, repo-level AI instructions, reusable SKILL.md files, sub-agent role definitions, AI-assisted coding, testing, documentation, governance checklists, and IDE-native delivery workflows.
The program introduces modern AI engineering concepts such as Spec Driven Development, BMAD-style agentic Agile delivery, AGENTS.md, skills, sub-agents, coding-agent workflows, MCP concepts, governance, quality gates, and productivity measurement.
Live Sessions Price:
For LIVE sessions – Offer price after discount is 300 USD 259 109 USD Or USD25000 INR 19900 INR 8900 Rupees
OR
Free Demo Session:
Indian Timings: 15th June @ 9 PM – 10 PM (IST) (Indian Timings)
U.S Timings: 15th June @ 11:30 AM – 12:30 PM (EST) (U.S Timings)
U.K Timings: 15th June @ 4:30 PM – 5:30 PM (BST) (UK Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 9 PM – 10 PM (IST)
For Participants in the US: Monday to Friday @ 11:30 AM – 12:30 PM (EST)
For Participants in the UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)
What will I learn by the end of this course?
- Understand the concept of AI First
- Identify where AI can improve software
- Use structured prompting and context engineering for SDLC
- Apply Spec Driven Development to AI-assisted
- Understand BMAD-style agentic Agile
- Create AI-ready specifications, plans, and task
- Create repo-level instruction files such as md.
- Design reusable md files for engineering tasks.
- Define sub-agent roles such as architect, QA, security, documentation, and release
- Understand how IDE-native agentic workflows
- Use AI for requirements, design, coding, testing, documentation, and release
- Understand MCP concepts and how agents connect to tools and engineering
- Apply governance, security, privacy, and quality gate
- Measure AI impact using practical productivity and quality
- Present an AI First Engineering capstone workflow for interviews or workplace
What student’s have to say about Sabrish Surender:
| The trainer explained AI First Engineering concepts with excellent real-time examples, practical workflows, and strong industry knowledge. – Ram
The trainer explained all concepts in a very clear and practical manner with real-time industry examples. The sessions on AI First Engineering, Prompt Engineering, Spec Driven Development, and Agentic Workflows were highly informative and easy to understand. Hands-on demonstrations and project-based explanations made the learning experience very engaging and useful. – Micheal The course content was very well structured and covered modern AI engineering concepts in a practical way. The trainer explained every module clearly with real-time examples and interactive sessions. I gained strong confidence in using AI tools and workflows in software projects. – Priya One of the best AI-related trainings I attended with practical implementation and industry-oriented explanations. The trainer guided us step-by-step through prompts, specs, agents, testing, and AI-native development workflows. – Verma The trainer delivered the course in a very practical and industry-focused manner. Every module was explained clearly with real-time examples and hands-on demonstrations. The sessions on AI workflows, Prompt Engineering, and Spec Driven Development were especially useful. The trainer was highly interactive and answered all questions patiently. This course helped me understand how AI can improve modern software engineering practices. – Vysali |
Salient Features:
- 40 Hours of Live AI First Engineering Training with real-time project implementation
- Lifetime access to recorded sessions for continuous learning and revision
- Hands-on practical sessions with real-world performance testing scenarios
Who can enroll in this course?
- Software developers
- QA engineers
- SDETs
- Automation testers
- Business analysts
- Product owners
- Scrum masters
- DevOps engineers
- Cloud engineers
- Technical leads
- Engineering managers
- Final-year CS / IT / AIML students with project exposure
- Professionals interested in AI-enabled software delivery
Course syllabus:
Module 1: AI First Engineering Mindset and Operating Model(2 hours)
- What is AI First Engineering?
- Difference between AI-assisted and AI-first delivery
- Why software engineering is changing with GenAI
- Human + AI + agent collaboration model
- Role of developers, testers, BAs, architects, and managers in AI-first teams
- Where AI helps and where human control is required
- AI First Engineer as an emerging capability
Module 2: AI Across SDLC and Value Stream Redesign(3 hours)
- AI use cases across requirements, design, coding, testing, DevOps, documentation, and release
- Identifying repetitive and high-value engineering tasks
- SDLC bottlenecks, rework points, and handoff delays
- AI opportunity mapping
- Human approval checkpoints
- Task value vs token cost
- Productivity and quality improvement areas
Module 3: Prompt Engineering and Context Engineering for Software Teams(4 hours)
- Why generic prompts fail
- Role-context-task-constraint-output prompt structure
- Context engineering for project work
- Project context packs
- Structured outputs: Markdown, JSON, tables, checklists
- Prompt patterns for developers, QA, BAs, architects, and tech leads
- Prompt validation and review techniques
- Prompt anti-patterns
Module 4: Spec Driven Development(4 hours)
- Why direct “build this app” prompting fails
- Spec-first delivery approach
- Business idea to product specification
- Product specification to technical plan
- Technical plan to task breakdown
- Acceptance criteria and validation rules
- Traceability from requirement to test
- Using AI agents against specs
- Spec-driven feature delivery workflow
Module 5: BMAD and Agentic Agile Delivery(3 hours)
- What is BMAD-style agentic delivery?
- Role-based AI workflows
- Analyst agent
- Product manager agent
- Architect agent
- Scrum master agent
- Developer agent
- QA agent
- Documentation agent
- How agent roles pass context through artifacts
- BMAD vs Spec Driven Development
- Lightweight BMAD-inspired workflow for teams
Module 6: Repo-Native AI Instructions and Project Memory (4 hours)
- Why repo-level AI instructions matter
- md
- Copilot-style project instructions
- Cursor rules
- Gemini project instructions
- Claude-style project memory
- Instruction hierarchy
- Coding standards
- Testing rules
- Documentation rules
- Security rules
- Definition of Done for AI-generated work
Module 7: Skills, Sub-Agents, Commands and Hooks (5 hours)
- Difference between assistant, agent, sub-agent, skill, command, and hook
- What is a md file?
- Designing reusable engineering skills
- Skill examples: spec writer, test generator, code reviewer, release notes, documentation
- Sub-agent role definitions
- Architect agent
- QA agent
- Security agent
- Documentation agent
- Release readiness agent
- When to use a skill vs sub-agent
- How commands and hooks support repeatable workflows
- Human approval boundaries
Module 8: AI-Native IDE and Agentic Workflow Execution(4 hours)
- IDE as the AI First Engineering cockpit
- How modern AI IDEs differ from traditional IDEs
- VS Code as base development environment
- Codex-style coding-agent workflow
- Antigravity-style agentic IDE workflow
- Cursor and Claude Code as optional references
- How agents read repo context
- How agents use specs, instructions, and skills
- Reviewing AI-generated diffs
- Running tests before accepting changes
- Maintaining human control
- IDE-native workflow example: requirement, spec, plan, tasks, agent execution, review, test, documentation, release note
Module 9: AI-Assisted Coding, Testing, Review and Documentation(4 hours)
- AI for code understanding
- AI for code generation
- AI for refactoring
- AI for debugging
- AI for code review
- AI for unit test generation
- AI for API test generation
- AI for documentation
- AI for release notes
- Risks of hallucinated code
- How to review AI-generated outputs
- How to create quality gates before merge or release
Module 10: MCP, Integrations and Engineering Knowledge(2 hours)
- What is MCP?
- Why AI agents need tool and data access
- MCP client/server concept
- GitHub integration use cases
- Filesystem use cases
- Jira / Azure DevOps use cases
- Database use cases
- Browser/search use cases
- RAG vs MCP
- Engineering knowledge base
- Safe integration principles
Module 11: Governance, Security, Evals and Quality Gates(3 hours)
- Safe AI usage in engineering
- Client data protection
- Source code leakage risks
- Secrets leakage
- PII / PHI handling
- IP and copyright concerns
- Human approval gates
- Prompt injection awareness
- Agent permissions
- AI coding policy
- Evaluation basics
- Test and review gates
- Cost per task vs token cost
- Productivity metrics
- Quality metrics
Module 12: Capstone Build-in-Class and Interview / Workplace Readiness(2 hours)
- Capstone structure
- How to present AI First Engineering workflow
- How to explain specs, skills, agents, and IDE workflow
- Resume/project bullet creation
- Interview explanation
- Workplace adoption pitch
- Final review
FAQ’s :
1. What is AI First Engineering?
AI First Engineering is a modern software development approach where AI tools, agents, and workflows are integrated into the complete SDLC process.2. What will I learn in this course?
You will learn Prompt Engineering, Spec Driven Development, AI-assisted coding, AI Testing, Agentic Workflows, MCP, Governance, AI-native IDE workflows, and AI-assisted SDLC practices.3. Is this course suitable for beginners?
Yes, the course starts from fundamentals and gradually moves to advanced practical concepts.4. Is coding knowledge mandatory?
Basic software development or testing knowledge is recommended but not mandatory.5. Will real-time project workflows be covered?
Yes, the course includes practical project workflows, live demonstrations, and hands-on implementation.6. What are Agentic Workflows?
Agentic Workflows are AI-driven workflows where different AI agents perform roles like Developer, QA, Architect, Analyst, and Documentation support.7. What is Spec Driven Development?
Spec Driven Development is a process where product specifications, technical plans, tasks, and validations are created before development starts.8. Will Prompt Engineering be explained practically?
Yes, practical prompt writing, context engineering, structured prompts, and prompt validation techniques will be demonstrated.9. Which AI tools are discussed in the course?
The course covers AI-native IDEs, GitHub Copilot, Cursor-style workflows, Claude-style memory workflows, MCP integrations, and AI coding assistants.10. Will AI Testing concepts be covered?
Yes, AI-assisted testing, test generation, validation workflows, hallucination risks, and quality gates are covered.11. What is MCP in AI Engineering?
MCP (Model Context Protocol) helps AI agents securely connect with tools, repositories, databases, and engineering systems.12. Will this course help in workplace projects?
Yes, the course is designed based on modern industry workflows and real software engineering practices.How can I enroll for this course?
For LIVE sessions – Offer price after discount is 3
00 USD 259109 USD OrUSD25000 INR19900 INR8900 RupeesOR
For any other details, Call me or Whatsapp me on +91-9133190573