The EXACT System I use to Build AI Agent Teams with Cursor AI
AI Summary
Summary of AI Agent Team Building Process
Introduction
- The video covers an advanced process for building and managing AI agent teams.
- The creator suggests joining their community page for updates and resources.
Key Challenges in Building and Selling AI Agents
- Automation errors occur randomly and need long-term maintenance.
- Managing multiple APIs is complex and requires a system to handle updates and changes.
- Moving data between different client systems can be challenging.
- Changing requirements (scope creep) can make it difficult to adapt without overhauling the system.
- Sharing workflows with clients can lead to versioning and support issues.
Proposed Solution: Agent Access Model
- Transitioning from agency model to Agent access, which packages workflows and agents for easy client use.
- The model is credit-based, self-service, and removes the need for clients to understand prompt engineering.
- The backend is managed, focusing on integration and correct connections, while the front end interacts with clients.
Example: MVP Strategist Agent
- Demonstrates an agent that helps with MVP PRDs (Product Requirement Documents).
- The agent is simple, uses OpenAI’s API, and is designed to be extended later.
- The process involves an intake form, order placement, and a background job for processing.
Backend Architecture
- Uses a “Backend for Frontend” pattern, decoupling UI and data model.
- The system includes a Next.js app on a CDN (CloudFront), with SST for deployment.
- API Gateway handles requests and responses.
- Lambda functions are used for routing and task execution.
- An agent layer listens for messages on a queue and performs downstream tasks.
- DynamoDB tables store data, segregated by user ID.
- Event-driven architecture with a mix of request/response and decoupled events.
Management with Cursor (IDE)
- Uses SST console for managing AWS resources.
- The system is cost-effective, with detailed cost estimates provided by Cloudcraft.
- Cursor is used for code organization and Git strategy, separating concerns for API, dashboard, landing pages, and specifications.
Deployment Process
- Demonstrates a deployment process by renaming an agent and updating the system.
- The process is calm and automated, with SST handling deployments after Git commits.
- Emphasizes the importance of a structured process for managing changes and deployments.
Conclusion
- Encourages viewers to document and streamline their processes for building AI agent teams.
- Offers resources and templates in the video description for those interested in adopting the demonstrated system.
Detailed Instructions and URLs
- No specific CLI commands, website URLs, or detailed instructions were provided in the summary.