Day 43 of 100 Days Agentic Engineer Challenge: AI Agent and FastAPI

I’m continuing my work on InfluenceFlow AI Agent, and this is part two of my development log. Today, I’ll dive deeper into the Python and FastAPI implementation. In my previous post, I outlined the technology stack, but this time, I’ll focus more on the backend details. Before we get into that, let’s take a quick look at my daily task routine.

Daily Tasks Routine

💪 Physical Activity — I did 50 squats.
😴 Seven Hours of Sleep — I slept for 7 hours.
🤖 AI Agent — I started building real agents.
⏳ PAIC — In queue.
📊 Data Science — In queue.

If you want to know what all these tasks are about, read the introduction to the 100 Days Agentic Engineer Challenge.

FastAPI — perfect backend for Agentic Systems

FastAPI is a standout choice for building agentic systems and AI agent solutions for several reasons:

1. Asynchronous and Background Task Support
   Agentic systems rely heavily on backend operations where the user triggers an agent, and the heavy lifting happens in the background. FastAPI’s built-in support for asynchronous programming and its simple API for scheduling background tasks (using the BackgroundTasks class) means you can easily offload long-running processes without blocking the main request–response cycle. This is crucial for agents that must process data, trigger workflows, or interact with other systems after the initial input is provided.
2. Robust and Flexible REST API
   Since agentic systems often need to interface with a variety of inputs — whether it’s a front-end interface or messaging platforms like Slack and Discord — a solid REST API is essential. FastAPI shines in this area by providing automatic interactive documentation (via Swagger UI and ReDoc) and by making it straightforward to build, test, and maintain your endpoints. This flexibility ensures that your system can easily adapt to different input methods without being tightly coupled to a specific front-end solution.
3. Developer Productivity and Ease of Use
   FastAPI leverages modern Python type hints, which allows for both automatic validation of incoming data and clear, maintainable code. This focus on developer experience means that building and iterating on complex backend tasks — like those found in agentic systems — can be done quickly and with fewer errors. It also promotes a clean separation between input handling and background processing, aligning perfectly with the architecture where the user provides initial data and the heavy lifting is handled asynchronously.
4. High Performance and Scalability
   Built on top of Starlette and using Uvicorn as the ASGI server, FastAPI is designed to handle high-performance operations out of the box. This is particularly important for agentic systems where multiple agents might be running concurrently or processing large volumes of data. FastAPI’s asynchronous nature ensures that your backend can scale effectively, handling numerous simultaneous operations without performance degradation.
5. PydanticAI Integration for Generative AI Agents
   Extending FastAPI’s capabilities into the realm of generative AI, PydanticAI brings a “FastAPI feeling” to AI agent development. Built by the team behind Pydantic, it leverages robust type safety and data validation to ensure that AI agents deliver structured, reliable responses. PydanticAI also offers dynamic dependency injection and tooling support — allowing agents to seamlessly integrate external data sources and invoke specific tools during their operation. Additionally, features like real-time debugging and monitoring (via integrations such as Pydantic Logfire) further streamline the development and maintenance of sophisticated AI-driven workflows. This seamless integration means you can combine FastAPI’s powerful REST API capabilities with advanced AI functionalities to build comprehensive, production-grade agentic systems.

In Summary

Agentic systems require a backend that can efficiently handle background tasks and interact with multiple interfaces — sometimes even without a dedicated front-end. FastAPI meets these demands with:

• Seamless asynchronous processing that supports background tasks.
• A robust, flexible REST API that can integrate with various communication platforms.
• Enhanced developer productivity thanks to modern Python features and automatic documentation.
• High performance and scalability to manage concurrent, high-volume operations.
• Integrated support for generative AI via PydanticAI, ensuring type safety, dynamic dependency injection, and advanced tooling for building reliable AI agents.

These combined features make FastAPI an ideal framework for building agentic systems and AI agent solutions where the backend is the cornerstone of the application.

Status of the AI Agent

I’m still struggling with Twitter/X OAuth 2.0. Integrating and testing it locally hasn’t been easy. While I successfully implemented the 1.1 API version, working with the X API v2 is proving to be more time-consuming.

✅ FastAPI and dependencies — Installed
✅ Svelte and dependencies — Installed
✅ UI — Onboarding Page — Completed (Users can input their expertise or business category and target follower count)
✅ UI — Dashboard Page — Completed (Displays user-provided data and Facebook-related insights)
✅ Integration of News API — Completed
✅ Integration of Azure OpenAI API — Completed
✅ Strategy Generation — Completed
⚠️ Integration of X_com API — In progress (Encountering authentication issues with X API v2)

I’ll continue refining the agent and sharing daily updates until it’s fully operational.