AI - Design

AI Agent Design is an approach to developing artificial intelligence-based applications that puts the agent's workflow at the center, rather than focusing heavily on internal training logic or complex algorithms.

In other words, adopting this method shifts the focus from “how AI works internally” to the more practical “what the agent does and how it interacts with the user.”

Workflow vs Training: a new paradigm

With AI Agent Design, instead of spending time and energy primarily on advanced training techniques or highly complex AI models, the operative workflow of your agent takes center stage right from the outset.
This clearly isolates the agent’s tasks and precisely defines its structure and method of interaction with the end user.

Specifically, we clearly define:

• Agent architecture: the logical and functional organization of the intelligent system.
• Agent development: simple and targeted technical implementation of the AI’s specific workflow.
• API exposure: the ways the agent easily interacts with other software.
• User interface: designing a clear, effective, and distinctive user experience.

Advantages of AI Agent Design for entrepreneurs and startups

This approach offers numerous benefits:

• Faster development: by clearly isolating operational logic, you significantly reduce the time required to build and release functional prototypes and product iterations.
• Clearer objectives: defining the workflow early on provides better visibility into how the solution actually behaves and meets real user demands.
• Simple integration: clear API exposure allows easy integration into existing digital workflows and ecosystems.

In conclusion, adopting this AI Agent Design approach allows innovative companies to effectively focus on real business outcomes and the impact the intelligent agent will have on users.

With this new paradigm, entrepreneurs and startups can develop intelligent applications that are truly useful, effective, and ready to scale over time.

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In this architecture, we’ll develop a scalable, maintainable application using Next.js for the frontend and Python Flask for the backend, while utilizing JWT (JSON Web Tokens) for user authentication.

Frontend: Next.js with App Router

Next.js provides an excellent environment for building modern, dynamic user interfaces. The App Router is particularly useful for developing services like authentication and managing API calls directly from the frontend.

Key features:

• Next.js App Router: Use it to define API routes directly inside your Next.js application. This allows you to handle simple services like authentication, registration, and other frontend-side functionality without needing a separate backend.
• API Routes in Next.js: Next.js offers built-in API routes (under the pages/api folder) for backend-like functionality, which is perfect for dealing with authentication services like user login, registration, password management, etc.
• Authentication Integration: You can leverage external services like Supabase for authentication, and once the user is authenticated, you can manage JWT tokens on the frontend to make authenticated requests to the backend.

Example API Routes:

• /api/auth/login: Handles user login and returns a JWT.
• /api/auth/logout: Logs out the user, removing their JWT token.
• /api/user/profile: Fetches the authenticated user’s profile data using the JWT.

Backend: Python Flask for AI Agent and Business Logic

Python Flask serves as the backend to handle heavier business logic, such as managing the AI agent or other data-processing services. It will expose APIs for the frontend to interact with. Flask is an excellent choice for this as it is lightweight, flexible, and well-suited for API development.

Key features:

• Flask API: Flask will handle backend operations such as serving an AI agent, data management, or other complex services that the frontend needs to consume.
• JWT Authentication: Flask will implement middleware to validate the JWT token passed from the frontend with every API request. This ensures that only authenticated users can access certain routes or data.

Example Flask API:

• /api/ai/agent: Calls the AI agent to process a request and return a response.
• /api/user/settings: Retrieves user-specific data, protected by JWT authentication.

JWT Authentication Flow

• Frontend (Next.js): After the user authenticates via an external service like Supabase, the frontend will store the JWT token (typically in HTTP-only cookies or local storage).
• Backend (Flask): On each API request from the frontend, the Flask server will receive the token and validate it using a library like PyJWT or python-jose. If the token is valid, the server will process the request; if not, it will reject the request with a 401 Unauthorized error.

The backend doesn’t need to contact Supabase directly after the initial authentication. Instead, the server can simply validate the token locally using Supabase's public key or the JWT secret provided during setup.

Advantages of this Architecture:

Separation of Concerns: The frontend handles the UI and basic services like authentication, while the backend focuses on business logic and API exposure.

Scalability: Both the frontend and backend can be scaled independently. For example, you can scale the Flask service to handle more AI requests without worrying about the Next.js frontend.

Security: By using JWT authentication, you ensure that API access is secure and can easily be integrated with other external services like Supabase or Firebase for authentication.

Maintainability: With clear boundaries between frontend and backend, each component of the app can be maintained or replaced independently.

This architecture is well-suited for applications where you want to have a clean separation of concerns between your UI and backend services while keeping everything within a single repository for easy management. By using Next.js for the frontend and Flask for the backend, combined with JWT authentication, you have a robust, scalable, and secure setup for modern web applications.