🤖 Explicar con IA

AI Studio Pods¶

Pods are autonomous AI project teams that work on your objectives. Each pod operates as an independent unit with its own goals, tasks, team members, and file resources.

What is a Pod?¶

A pod is a self-contained AI project environment:

• Objective — The high-level goal the pod works toward
• Tasks — Specific work items broken down from the objective
• Team Members — AI agents and human collaborators assigned to the pod
• Files — Documents, code, and resources attached to the pod
• Token Budget — Allocated AI compute for the pod

Creating a Pod¶

1. Navigate to AI Studio
2. Click Deploy New Pod
3. Configure:
4. Pod Name — Descriptive name for the project
5. Objective — High-level goal (e.g., “Build a REST API for inventory management”)
6. Initial Tasks — One or more starting tasks
7. Team Members — Add AI agents or human collaborators
8. Model Preference — Select AI model (optional)
9. Click Deploy

The pod is created and begins working on the initial tasks.

Pod Structure¶

Objective¶

The objective defines the pod’s mission:

Example: "Migrate the legacy PHP monolith to a microservices architecture 
using Node.js and deploy to AWS"

The objective guides task prioritization and AI decision-making.

Tasks¶

Tasks are the individual work items within a pod:

Adding Tasks¶

1. Open the pod
2. Click Add Task
3. Fill in task details
4. Assign to a team member or leave unassigned
5. Click Save

Task Status Flow¶

graph LR
    A[Pending] --> B[In Progress]
    B --> C[Review]
    C --> D[Complete]
    C --> B

Team Members¶

Each pod can have multiple team members:

Adding Team Members¶

1. Open the pod
2. Click Team
3. Click Add Member
4. Search for a person or AI agent
5. Set their role (Contributor, Reviewer, Viewer)
6. Click Add

File Attachments¶

Attach files to provide context to the pod:

Attaching Files¶

1. Open the pod
2. Click Files
3. Click Upload
4. Select files from your computer
5. Files are stored securely in the ai-studio-files bucket

Files are accessible to all pod members and are used as context for AI operations.

Pod Dashboard¶

The pod dashboard shows:

Multi-Model Support¶

Pods can leverage multiple AI models:

Model Selection¶

• Automatic — The system selects the best model per task (default)
• Manual — Specify a preferred model when creating tasks
• Per-Pod — Set a default model preference for the entire pod

Token Usage¶

Each pod tracks its own token consumption:

View detailed token usage in the Token Usage section.

Pod Lifecycle¶

Active¶

The pod is actively working on tasks. AI agents process tasks, team members collaborate, and files are managed.

Paused¶

The pod is temporarily suspended:

• No new AI processing occurs
• Existing work is preserved
• Team members can still review completed tasks
• Token consumption stops

Archived¶

The pod is completed and archived:

• All work is preserved for reference
• No further modifications
• Files remain accessible
• Token usage history is retained

Managing Pods¶

Duplicate a Pod¶

Create a copy of an existing pod:

1. Open the pod
2. Click Duplicate
3. Modify the name and objective
4. Click Create

The new pod inherits tasks, files, and team members.

Export Pod Data¶

Export all pod data for backup or migration:

1. Open the pod
2. Click Export
3. Select data to export:
4. Tasks and status
5. File list
6. Activity log
7. Token usage report
8. Download as JSON or CSV

Best Practices¶

1. Define clear objectives — Specific, measurable goals lead to better outcomes
2. Break down tasks — Smaller tasks are completed more reliably
3. Attach relevant files — Context improves AI performance
4. Review regularly — Check pod progress and redirect as needed
5. Monitor token usage — Stay within budget by tracking consumption

What’s Next¶

• Token Usage — Monitor AI compute consumption
• AI Gateway — Learn about the underlying infrastructure

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