Skip to content
Teaching · SMIT

Vibe Engineering with AI Coding Agents

A project-based path through vibe coding, vibe engineering, and agentic engineering, given room to breathe across a full eight weeks.

Role
Instructor
Institution
SMIT
Duration
8 weeks
Format
Project-based, live

Currently teaching2 instructional days per week, plus a weekly hands-on build session

Overview

What this course is

Vibe Engineering with AI Coding Agents takes the core ideas of agentic software development and gives them room to breathe across a full two months. Instead of rushing through tools in a single sitting, each stage gets dedicated time: more practice with every tool, more variations on every project, and full weeks set aside for topics a faster course would otherwise compress into a single afternoon.

The course follows a simple three-part arc. Learners begin with vibe coding, move into the more disciplined practice of vibe engineering, and finish with agentic engineering at an expert level. Each stage spans two to three weeks rather than a handful of days, with an added capstone week and a closing session on career paths and monetization.

The arc

Three stages, from vibe coding to a team of agents

Each stage gets two to three weeks, so nothing gets compressed into a single afternoon.

01

Vibe coding

Mental models, prompt and context engineering, and hands-on fluency with IDE-based agents such as Cursor, Copilot, Codex, and Antigravity.

02

Vibe engineering

The more disciplined practice: professional CLI workflows with Claude Code, MCP, Skills, Plugins, and real engineering-tool integrations.

03

Agentic engineering

Expert level: sub-agents, hooks, sandboxing, and multi-agent orchestration, culminating in a full team-of-agents capstone build.

Core tools you will use
Claude CodeCursorGitHub CopilotOpenAI CodexGoogle AntigravityOpenCodeAmp
Curriculum

Eight weeks, week by week

Two months, split into foundations and vibe coding, then vibe engineering and agentic mastery. Expand any week for the full breakdown.

Month One · Foundations and Vibe Coding

W1

Welcome to the Agentic Landscape

Building the mental models everything else relies on. No project this week, the focus is purely conceptual so later weeks move faster.

Day 1
Orientation and the Eight Levels of AI Adoption
  • A roadmap of how vibe coding, vibe engineering, and agentic engineering relate to one another
  • The origin of vibe coding and how it grew into a professional discipline
  • The eight levels of AI adoption, from basic prompting through autonomous multi-agent systems
  • The three surfaces for AI coding tools: IDEs, plugins, and CLIs
Day 2
How Large Language Models Actually Work
  • Tokens, context windows, and memory, explained from the ground up
  • Why agent output varies from run to run, and how to design around that variability
  • Hands-on: generate a working app from a single prompt to feel the unpredictability firsthand
W2

Prompt and Context Engineering

Giving agents the information they need to succeed.

Project 1: a personal website featuring an AI digital twin.
Day 1
Context Engineering Fundamentals
  • Writing effective prompts: specificity, constraints, and breaking work into small testable steps
  • Introducing AGENTS.md and CLAUDE.md as project-level instructions an agent reads first
  • Cross-checking agent answers across models to catch mistakes early
Day 2
Workshop: Building Your AI Digital Twin
  • Scaffold a personal website project with a clear AGENTS.md file
  • Iterate with an agent through cycles of planning, generation, and feedback
  • Review common failure modes and how targeted feedback resolves them
W3

Cursor and YOLO Mode

Working quickly inside an AI-native IDE.

Project 2: a Kanban-style project management app with drag and drop.
Day 1
Cursor in Depth
  • Cursor settings: autorun versus YOLO mode, and sandboxed versus unsandboxed execution
  • Using planning mode and iterative feedback loops to steer an agent toward a working build
Day 2
Workshop: Build the Kanban App
  • Build a complete drag-and-drop Kanban app from scratch using Cursor in YOLO mode
  • Debug and refine the codebase using specific, targeted feedback to the agent
W4

Copilot, Codex, and Antigravity

Comparing agents across IDEs and providers.

Project 3: a commercial-grade MVP, built end to end with the agent of your choice.
Day 1
Touring the Alternatives
  • GitHub Copilot's agent mode compared with Cursor's workflow
  • OpenAI Codex for autonomous task execution
  • Google Antigravity: rules, workflows, and parallel agent tasks
Day 2
Workshop: Ship a Commercial MVP
  • Choose a tool and scope a real MVP idea with a clear specification
  • Build, test, and polish the MVP end to end as a portfolio piece
  • A Month One retrospective comparing strengths and weaknesses across all four tools

Month Two · Vibe Engineering and Agentic Mastery

W5

Claude Code Fundamentals

Moving from IDE agents to professional CLI workflows.

Project 4: a SaaS legal assistant that drafts documents and exports them as PDFs.
Day 1
Claude Code Core Mechanics
  • Why CLI-based agentic workflows differ from IDE-based ones
  • Slash commands, checkpoints, and session management
  • Writing a precise CLAUDE.md for a real, multi-file project
Day 2
Workshop: Scaffold the SaaS Legal Assistant
  • Plan the legal assistant's architecture together with Claude Code
  • Generate a first working version of document drafting and PDF export
W6

MCP, Skills, Plugins, and Integrations

Extending what an agent can reach and do.

The build continues: connecting the SaaS legal assistant to real engineering tools.
Day 1
Model Context Protocol and Skills
  • MCP fundamentals: how agents connect to external tools and data
  • Packaging reusable Skills and Plugins for specialized, repeatable agent behavior
Day 2
Workshop: Jira and GitHub in the Loop
  • Raise a Jira issue and have Claude Code pick it up, build it, test it, and push the change to GitHub
  • Ralph Loops and other iterative delivery workflows for unattended progress
  • Debugging strategies suited to non-deterministic, agent-driven workflows
W7

Multi-Agent Systems and Sandboxing

Moving from a single agent to a coordinated team.

Capstone planning begins: the architecture for a realtime trading workstation.
Day 1
Sub-Agents, Hooks, and Sandboxes
  • Delegating work to sub-agents, and observing and controlling behavior with Hooks
  • Sandboxing: running agents in YOLO mode safely, without risk to the host system
  • Working with large, pre-existing codebases rather than greenfield projects
Day 2
Workshop: Orchestration Frameworks
  • Agent swarms and orchestration patterns, including Claude Agent Teams and Gas Town
  • A look at the frontier: Claude Agent SDK, Cowork, and OpenClaw
  • Design the multi-agent architecture for the capstone project
W8

Capstone Build and Career Paths

Bringing everything together.

Capstone: a realtime trading workstation with virtual trades on live market data and an AI trading strategy assistant, built by a coordinated team of agents.
Day 1
Workshop: Build the Trading Workstation
  • Stand up the realtime data feed, virtual trade engine, and AI assistant using a multi-agent team
  • Apply the sandboxing, hooks, and orchestration patterns from Week 7 to a real, complex build
Day 2
Capstone Review and What Comes Next
  • Present and review completed capstone projects
  • Turning agentic coding projects into a portfolio, freelance offerings, or a SaaS product
  • Where the field is heading, and how to keep pace with new tools after the course ends
Capstone

A trading workstation run by a team of agents

A realtime trading workstation with virtual trades on live market data and an AI strategy assistant, built and operated by a coordinated team of AI agents.

Five portfolio-ready projects
  1. 1An AI digital twin personal website
  2. 2A drag-and-drop Kanban app
  3. 3A commercial-grade MVP
  4. 4A SaaS legal assistant with PDF export
  5. 5A realtime trading workstation
What you walk away with
  • Practical fluency across both IDE-based agents (Cursor, Copilot, Codex, Antigravity) and CLI-based agents (Claude Code, OpenCode, Amp)
  • A working mental model of how language-model agents reason, use context, and vary between runs
  • Five complete, portfolio-ready projects, from an AI digital twin site to a realtime trading workstation
  • Production-level practices: MCP, Skills, Plugins, Hooks, sub-agents, sandboxing, and multi-agent orchestration
  • The ability to scale from working solo with one agent to directing a coordinated team of agents on a single codebase
  • A concrete plan for turning these skills into freelance work, a job-ready portfolio, or a SaaS product of your own

Let's build something that ships.

Hiring for a senior Data/AI role, or need a data platform that actually holds up in production? Let's talk.

or email me directly at muhammaduzairkhan329@gmail.com