Elementor #676

Agentic RAG — Intelligent Assistant for Agile Project
Management

This application is built upon an Agentic RAG (Retrieval-Augmented Generation with Agentic Reasoning) architecture, combining the reasoning capabilities of large language models with autonomous decision-making and structured information retrieval.

Instead of a single static model answering all queries, the system dynamically routes the user’s question through multiple reasoning agents, each with a specialized function — such as retrieval, grading, rewriting, and answer generation. This agent-oriented workflow allows the model to:

• decide whether additional context is needed,
• fetch and evaluate relevant project information, and
• synthesize a precise and context-aware response.

The application enables Agile Project Managers to interact with project documentation, user stories, tasks, and acceptance criteria in natural language — transforming raw textual data into actionable insights and structured understanding.

Core Technologies and Architecture:

• Agentic RAG: A multi-stage reasoning framework integrating LLMs with retrieval, reflection, and adaptive question-rewriting loops.
• LangGraph: Used to orchestrate the multi-agent workflow graph (Router → Retriever → Grader → Rewriter → Generator) ensuring controllable and explainable execution flow.
• LangChain + OpenAI Models: Powering reasoning and generation through GPT-4o and text-embedding-3-large for deep semantic understanding.
• FAISS Vector Store: Providing high-speed vector search and similarity-based retrieval across project knowledge bases.
• Streamlit Framework: Delivering an interactive, browser-based environment for seamless human-AI collaboration.

This project showcases a practical implementation of human-centered, agentic AI within Agile Software Engineering — a step toward autonomous assistants capable of supporting project planning, backlog refinement, and intelligent decision-making in complex collaborative environments.