Optimizing Server Architecture for Scalable Deployment of AI Agents

Creating AI agents and RAG pipelines using LLMs is one thing—but deploying them on a server for real-world use and scalability is a whole different challenge.

Once you've built and tested your AI worker locally, the next step is to wrap it in an API, typically using something like FastAPI. This allows you to interact with your AI agent over HTTP, making it accessible to frontend apps, automation tools, or other services.

Now comes the deployment part. And here, you generally have two approaches:

1. Naive Deployment – quick and simple, good for early testing or prototypes.

2. Optimized Deployment – designed for scalability, stability, and production readiness.

To make this more concrete, let's walk through an example: a Resume Analyzer RAG agent.

Naive Approach:

Here, the user might think that all he needs to do is deploy his FastAPI on the server. After that, he would upload his resume, and the AI processing would take place on the server. Once completed, the output would be displayed to the user on the front end.

This we call the Synchronous Approach.

Issues here:

1. LLM Rate limit

2. Request Time out

3. The users can't track their request or how long to wait

4. Overload the server in case of multiple users simultaneously using the service.

Optimized Approach:

Here, the user needs to consider what if multiple users are using his service at the same time, how will it handle multiple requests, tracking them, processing them, without server overload, exceeding LLM rate limit, etc. We handle things a little smartly here:

Process:

1. Resume Upload
   The user uploads their resume to the server.
   We store the file in a storage system (like S3 or local storage) and return a unique file_id to the user.

2. Queueing & Tracking
   The uploaded file is added to a processing queue—let’s say a Redis Queue.
   At the same time, we create a record in a MongoDB database with the file's details and its initial status (e.g., Queue).

3. Background Processing
   One by one, files from the queue are sent to the AI Worker for processing.
   As each step progresses, we update the status in the database so the user can always check the current state using their file_id.

4. Completion & Next Job
   Once processing is complete, we update the status to Done in the database.
   Then, the next file in the queue is picked up for processing.

This way we handle it asynchronously on the server.

How its Optimized?

1. Our server is currently designed to add files to a queue without processing them immediately, which means there's no significant load on the server.

2. The LLM rate limit will not be an issue since we are processing one file at a time.

3. Scalable: In the future, we can add more AI workers to process multiple files simultaneously without any interference between them.

4. For the user, they can check the status of their file at any time, so there’s no need to wait around while the file is being processed. Users can simply return later and retrieve their file using the file ID provided.

Summary:

Just shared my understanding of how we can deploy your AI Workers—not just by building powerful RAGs or LLM agents, but by wrapping them in APIs and integrating them into a scalable server-side architecture using queues, databases, and background processing to ensure reliability, trackability, and real-world usability.

Credits:

Credits: I am very grateful to ChaiCode for providing all this knowledge, Insights, and Deep Learning about AI: Piyush Garg Hitesh Choudhary

If you want to learn too, you can join here → Cohort || Apply from ChaiCode & Use NAKUL51937 to get 10% off

Thanks:

🙏 Thanks for giving your Precious time, reading this article.

Don't Forget to Like the Article.

Feel free to comment your thoughts, would love to hear your feedback...

Connect:

Let’s learn something together: LinkedIn, Twitter

If you would like, you can check out my Portfolio