Agentic AI Document Generation Platform

The Challenge

Knowledge workers spend significant time synthesizing information from disparate sources—technical reports, PDFs, presentations—into focused documents. Traditional solutions fall short: basic chatbots can't maintain long conversations, simple RAG systems lack autonomy, and most AI writing tools are glorified autocomplete.

The opportunity was clear: build an agentic AI system that could reason, act autonomously, and deliver production-quality documents. Not a chatbot—a true AI collaborator.

Solution Overview

I designed and built a complete, production-grade web application that transforms document synthesis workflows. The platform combines an interactive frontend, high-performance API backend, and sophisticated AI agent orchestration.

Key Outcomes: - Transforms multi-day manual synthesis into streamlined interactive sessions - Enables autonomous multi-step document creation from high-level commands - Provides secure, multi-user platform with session persistence

Technical Approach

Agentic AI with LangGraph

Traditional LLM applications follow basic request-response patterns that break down for complex, multi-step tasks. I needed the AI to maintain context across conversations, use tools autonomously, and loop through reasoning cycles.

LangGraph provides exactly this through its cyclical, stateful graph architecture. The agent operates as a directed graph with nodes for: - Context initialization: Loading source documents for RAG - Reasoning: LLM with tool-using capability analyzing user intent - Action execution: Autonomous tool invocation with results fed back into state

The key innovation: after each reasoning step, conditional logic determines whether to loop back for more reasoning or return to the user. A single command like "Draft a technical report on X" triggers an autonomous chain—reading sources, drafting sections, refining content—all without manual intervention.

RAG Implementation

I built a Retrieval-Augmented Generation pipeline handling multiple document formats: - Word documents, PowerPoint, and PDFs processed into clean markdown - Context-aware retrieval enabling the agent to reference source material - Optimized for typical enterprise document collections

Full-Stack Production Engineering

Backend (FastAPI): - RESTful API with async capabilities - JWT-based authentication with secure credential management - Relational database with SQLAlchemy ORM for users, sessions, and documents - Content extraction pipeline using python-docx, python-pptx, and PyMuPDF

Frontend (Streamlit): - Multi-panel workspace for source management, chat, and document preview - Real-time markdown rendering during generation - File upload/download workflows

Infrastructure (Docker): - Containerized multi-service deployment - Reverse proxy with WebSocket support - Health monitoring and automated recovery

Skills Demonstrated

Agentic AI & LLM Engineering: - LangGraph stateful agent design with cyclical reasoning - Tool-using LLM patterns - RAG implementation across multiple document formats - Prompt engineering for multi-step tasks

Full-Stack Development: - FastAPI async API design - SQLAlchemy ORM and relational modeling - JWT authentication implementation - Streamlit interactive UI

Production Engineering: - Docker containerization and orchestration - Deployment automation - Security-first design patterns

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Code Repository: Private (proprietary internal tool)

Live Demo: Available upon request for interview processes

For questions about agentic AI system design, contact me.

Related Projects: - Scientific Computing Framework - JAX-based high-performance optimization - Queensland Rail Predictive Analytics - Production ML deployment - Model Risk & Decision Support - Bayesian decision theory