Slide 1. What Is an AI Coding Agent?

An AI Coding Agent is more than an AI that generates code.

It is an AI system that can understand a development task, explore a codebase, modify files, execute commands, run tests, analyze errors, and iterate toward a solution.

Traditional AI Coding Assistant

Developer
    ↓
"Write this code"
    ↓
AI
    ↓
Code suggestion
    ↓
Developer applies it

AI Coding Agent

Developer
    ↓
"Add authentication"
    ↓
AI Agent
    ↓
Explore project
    ↓
Create a plan
    ↓
Modify multiple files
    ↓
Run tests
    ↓
Fix errors
    ↓
Report results

Slide 2. AI Assistant vs. AI Agent

AI Assistant

You ask AI to help you code.

"Complete this function."

"Explain this error."

"Create a React component."

The developer manages the overall workflow.

AI Coding Agent

You ask AI to perform a development task.

"Add authentication to this application."

"Create a dashboard and add tests."

"Find and fix the failing tests."

The Agent can perform multiple steps autonomously.

The Key Difference

Assistant = Help me code

Agent = Do this development task

Slide 3. What Can an Agent Do?

A coding agent can typically perform a workflow such as:

1. Explore the project
        ↓
2. Search relevant code
        ↓
3. Create an implementation plan
        ↓
4. Write / modify code
        ↓
5. Run terminal commands
        ↓
6. Run tests / build
        ↓
7. Analyze errors
        ↓
8. Fix the code
        ↓
9. Report the result

The key idea is:

An Agent performs a multi-step development workflow, not just a single code-generation request.

Slide 4. Popular AI Coding Agents

Examples include:

• Cursor Agent

• Claude Code

• OpenAI Codex

• GitHub Copilot Agent

• Windsurf

• Cline

• Roo Code

• Devin

• OpenHands

They can be roughly grouped by their primary workflow:

IDE-centered
  ↓
Cursor / Windsurf

Terminal-centered
  ↓
Claude Code / Codex

GitHub-centered
  ↓
GitHub Copilot Agent

Autonomous development
  ↓
Devin / OpenHands

Slide 5. Cursor Agent

Cursor is an AI-powered development environment with integrated coding agents.

┌───────────────────────────────┐
│           Cursor              │
│                               │
│  Code Editor    AI Agent      │
│                               │
│  app/           "Build this"  │
│  components/                  │
│  lib/                         │
│                               │
└───────────────────────────────┘

Features

• Explore the codebase

• Modify multiple files

• Run terminal commands

• Generate and refactor code

• Work alongside the developer inside the IDE

Best suited for

Developers who want to work with an AI Agent directly inside their development environment.

Slide 6. Claude Code

Claude Code is a terminal-oriented coding agent.

Terminal

$ claude

> Add authentication to this Next.js app.

Agent:
  Analyze
    ↓
  Modify
    ↓
  Test
    ↓
  Fix

Features

• Terminal-based workflow

• Codebase exploration

• Multi-file editing

• Command execution

• Testing and debugging

Best suited for

Developers who prefer a terminal-based development workflow.

Slide 7. OpenAI Codex

Codex is a coding-agent system designed to perform software development tasks.

A typical workflow looks like:

Task
 ↓
Explore repository
 ↓
Plan
 ↓
Implement
 ↓
Test
 ↓
Fix
 ↓
Result

The emphasis is not simply on generating code, but on completing a defined development task.

Best suited for

Developers who want to delegate development tasks to an AI Agent and review the result.

Slide 8. GitHub Copilot Agent

GitHub Copilot has evolved beyond code completion into agent-based development workflows.

A typical workflow can look like:

GitHub Issue
      ↓
Copilot Agent
      ↓
Explore Repository
      ↓
Modify Code
      ↓
Run Tests
      ↓
Pull Request
      ↓
Developer Review

Key idea

Issue → Agent → Pull Request → Human Review

This fits naturally into GitHub-based development teams.

Slide 9. Advantages of AI Coding Agents

1. Faster Development

Agents can automate repetitive coding tasks.

2. Multi-file Changes

A single feature may require changes across many files. Agents can handle these changes together.

3. Rapid Prototyping

Ideas can be turned into working MVPs much faster.

4. Testing and Debugging

Agents can run tests, inspect errors, and attempt fixes.

5. Excellent for Vibe Coding

Developers can describe requirements in natural language and iterate with the Agent.

Slide 10. Disadvantages of AI Coding Agents

1. They Can Generate a Large Amount of Wrong Code

Wrong assumption
      ↓
Multiple file changes
      ↓
Larger problem

The impact of an incorrect decision can be much larger than with a simple AI code suggestion.

2. Code Quality Is Not Guaranteed

Build succeeds
      ≠
Good architecture

Tests pass
      ≠
No security problems

3. Security Risks

Agents may read files, modify files, and execute commands.

4. Cost

Agents may perform many AI calls, code searches, tool executions, and iterations.

5. Technical Debt

Fast AI-generated code can become difficult to maintain if it is not reviewed and cleaned up.

Slide 11. Working Code ≠ Good Code

Suppose the Agent successfully produces:

✓ Build succeeds
✓ Tests pass
✓ UI works

This does not necessarily mean the application is high quality.

We still need to review:

Security
Architecture
Performance
Maintainability
Error Handling
Data Validation
Authentication
Authorization

A working application can still have serious technical problems.

Slide 12. AI Agents and Security

An Agent may have more access than a traditional chatbot.

Agent
 ↓
Read files
 ↓
Modify files
 ↓
Run commands
 ↓
Install packages
 ↓
Run tests

Therefore, developers should consider:

• Sensitive information

• API keys and secrets

• File permissions

• Terminal permissions

• Database permissions

• Git changes

• Generated code

Principle

Give the Agent enough access to work — but not more access than necessary.

Slide 13. The Ideal Human-Agent Workflow

A useful model is:

              HUMAN
                │
                │ What / Why
                ↓
           AI CODING AGENT
                │
                │ How
                ↓
              CODE
                │
                ↓
             TESTING
                │
                ↓
              HUMAN
             REVIEW

Human

• What should we build?

• Why do we need it?

• What user problem are we solving?

• What technology should we use?

• What are our quality and security requirements?

AI Agent

• Explore the codebase

• Implement

• Perform repetitive tasks

• Test

• Debug

• Refactor

Human

• Validate the result

• Review the code

• Check security

• Make product decisions

• Approve the final result

Slide 14. How Does the Developer's Role Change?

Traditional Development

Developer
    ↓
Write Code
    ↓
Test
    ↓
Debug
    ↓
Deploy

AI Agent Era

Developer
    ↓
Define Goal
    ↓
Design
    ↓
Direct Agent
    ↓
Review
    ↓
Approve

The developer's role increasingly moves from writing every line of code toward:

• Problem definition

• System design

• Task decomposition

• Agent direction

• Validation

• Quality control

Slide 15. AI Coding Agent Comparison

AgentPrimary EnvironmentMain Strength Cursor AgentIDECode editing + Agent collaboration Claude CodeTerminalTerminal-based development OpenAI CodexAgentSoftware development tasks GitHub Copilot AgentGitHubIssue → PR workflow WindsurfIDEAI-centered development ClineIDEFlexible Agent workflows Roo CodeIDECustomizable Agent workflows DevinCloud / AgentLonger autonomous tasks OpenHandsOpen SourceAutonomous development workflows

Note: Features and pricing change quickly. Always check the official documentation before selecting a tool for a project.

Slide 16. How Should You Choose?

There is no single best Agent for everyone.

If you want to code directly inside an IDE

Cursor / Windsurf

If you prefer a terminal workflow

Claude Code / Codex

If your team works primarily through GitHub

GitHub Copilot Agent

If you want to experiment with longer autonomous tasks

Devin / OpenHands

The important question is:

Which Agent fits naturally into your development workflow?

Slide 17. AI Coding Agents in the Vibe Coding Stack

Connecting this concept to our previous technology stack:

              PRODUCT IDEA
                   ↓
          ┌─────────────────┐
          │   AI Coding     │
          │     Agent       │
          └────────┬────────┘
                   ↓
              Next.js
                   ↓
               React
                   ↓
            Tailwind CSS
                   ↓
              Supabase
                   ↓
               Vercel
                   ↓
            LIVE PRODUCT

The AI Coding Agent becomes a new interface for performing software development work across this technology stack.

Slide 18. Key Takeaways

AI Coding Agent is not just autocomplete.

It is an AI system that can perform software-development tasks.

But the goal is not simply:

"AI replaces developers."

A more useful way to think about it is:

AI changes how developers work.

The key skills become:

Product Thinking
       +
Technical Understanding
       +
AI Agent Skills
       +
Human Review

Final Message

Don't just ask AI to write code.

Learn how to give AI a development task, guide the Agent, and review the result.