[100% Off] Ai-Powered Sdlc: Vibe Coding To Agentic Engineering
Build AI software workflows with coding agents, context engineering, tests, evals, guardrails, and human review
What you’ll learn
- Explain how AI is reshaping the SDLC
- from requirements and architecture to coding
- testing
- deployment
- and review.,Apply context engineering to give coding agents clear instructions
- constraints
- examples
- tools
- memory
- and guardrails.,Turn business ideas into AI-ready specifications
- user stories
- acceptance criteria
- edge cases
- API contracts
- and plans.,Design architecture-first workflows that preserve module boundaries
- code quality
- security
- maintainability
- and human control.,Build a coding-agent harness using instructions
- tools
- permissions
- sandboxes
- hooks
- orchestration
- and feedback loops.,Evaluate AI-generated software with automated reviews
- unit tests
- integration tests
- LLM evaluations
- and CI/CD quality gates.,Use human-in-the-loop review to delegate agent-sized tasks
- inspect outputs
- manage failures
- and approve production changes.,Assess AI development costs and ROI using token consumption
- retry loops
- model routing
- maintenance debt
- and observability.,Create an agentic SDLC playbook for adopting AI coding workflows safely across individuals
- teams
- and organizations.
Requirements
- No previous experience with AI coding agents or agentic engineering is required.,A basic understanding of software development concepts will be helpful
- but beginners can follow the course.,No advanced programming
- machine learning
- or data science knowledge is required.,A computer with internet access and an interest in modern AI-powered software development workflows.,Familiarity with coding
- testing
- Git
- or CI/CD is helpful but not mandatory.
Description
Software development is changing from manually writing every line of code to designing intelligent systems that can plan, generate, test, evaluate, and improve software.
In AI-Powered SDLC: Vibe Coding to Agentic Engineering, you will learn how to integrate AI coding assistants and autonomous coding agents across the complete software development lifecycle.
You will begin by understanding how software development is shifting from syntax-driven implementation to intent-driven engineering. You will then learn how context engineering, specifications, architectural constraints, agent harnesses, tools, tests, evaluations, guardrails, and human review work together to produce reliable software.
The course goes beyond basic prompt engineering and autocomplete. You will learn how to create structured workflows in which AI agents operate as implementation workers while developers remain responsible for architecture, quality, security, cost, and production readiness.
Throughout the course, you will explore practical topics including AI-friendly requirements, user stories, acceptance criteria, context files, coding-agent instructions, MCP tools, agent orchestration, sandboxing, automated testing, LLM evaluations, observability, CI/CD quality gates, token-cost management, and human-in-the-loop approvals.
Each major section includes a focused hands-on project lab. You will create an AI-SDLC workflow map, a feature specification pack, an agentic feature factory, a coding-agent harness, an AI code evaluation pipeline, a production-readiness review, an AI development cost calculator, and a complete agentic SDLC playbook.
By the end of the course, you will understand how to move beyond experimental vibe coding and build disciplined, scalable, and production-ready AI software engineering workflows.
This course is designed for software developers, technical leads, architects, engineering managers, DevOps professionals, AI engineers, and anyone interested in the future of software development.
