Kickoff: Como trabaja un equipo AI-native

🤖 Lesson: How an AI-Native Team Works

🎯 Lesson Objective

By the end of this session, you will understand how modern teams integrate AI into their entire workflow—and how to apply it to build faster, higher-quality, real-world products.

🧠 1. What does AI-Native mean?

An AI-Native team:

• Uses AI across every stage of development

• Automates repetitive and complex tasks

• Makes decisions supported by AI

• Collaborates with intelligent agents

👉 AI is not just a tool—it’s part of the team.

⚙️ 2. The AI-Native Workflow

🧩 2.1 Spec-Driven Development

Before writing code:

• Define the problem clearly

• Create specs (features, user stories, acceptance criteria)

• Use AI to refine and expand requirements

💡 Result: less ambiguity, less rework

🤖 2.2 Agentic Development Workflow

Create AI agents that handle specific tasks:

Examples:

• Code agent → generates features

• Testing agent → creates tests

• Documentation agent → writes docs

💡 Think of this as a “mini automated team”

💻 2.3 AI-Assisted Development

• Pair programming with AI

• Generate components, APIs, and logic

• Smart refactoring

💡 Developers focus on decisions, not repetitive work

🧪 2.4 TDD + Automated Testing

• Tests generated with AI

• Continuous validation (unit + E2E)

• Early bug detection

💡 Quality is built-in from the start

🔁 2.5 Continuous Iteration

• Short development cycles

• Fast feedback loops

• Real-time improvements

💡 Build → measure → improve (fast)

🧰 3. Key Tools in AI-Native Teams

🎙️ Content Generation

• ElevenLabs → realistic voice generation

• HeyGen → AI avatars & videos

• Nano Banana → creative content generation

💻 Development

• AI copilots

• Code generators

• Automated testing tools

📊 Project Management

• AI for task creation, documentation, tracking

• Workflow automation

👥 4. Roles in an AI-Native Team

• AI-Enhanced Developer → builds with AI assistance

• AI Product Builder → designs products with AI-first thinking

• AI Workflow Designer → creates agents and automation flows

• Human-in-the-loop → validates and makes key decisions

⚔️ 5. AI-Native vs Traditional Teams

Traditional. AI-Native

Manual coding. AI-assisted coding

Late testing. Continuous testing

Slow cycles. Rapid iteration

Fixed roles. Hybrid roles

Manual docs. AI-generated docs

🚀 6. Real Workflow Example

1. Define a feature

2. Generate specs with AI

3. Generate initial code

4. Generate tests automatically

5. Refine with human feedback

6. Deploy

7. Create marketing content with AI

🧪 7. Workshop Exercise

🛠️ Activity:

Simulate a mini AI-Native team

Steps:

1. Define a simple feature (e.g., authentication login)

2. Use AI to generate:

   • Spec (user story + acceptance criteria)

   • Base code (frontend or backend)

   • Tests

3. Iterate and improve with AI

4. Explain what the AI did vs what you did

🧩 8. Key Takeaway

An AI-Native team:

• Doesn’t replace humans

• Amplifies their capabilities

• Enables faster and better product development

👉 The real advantage isn’t just using AI…
it’s knowing how to work with AI as a team.