How to Create an AI Agent that Reads Legislation and Summarizes Each Document

This project builds an autonomous AI agent for dynamic project management using Python, large language models, and data visualization tools. It starts by parsing high-level goals, constraints, and timelines to automatically generate project plans, supported by tools like LangChain, Plotly, and NetworkX for Gantt charts and dependency graphs. Task dependencies are defined and visualized, enabling structured scheduling and clear workflow mapping. The system detects delays by comparing current progress against expected timelines and flags overdue or underperforming tasks. When delays are found, a resource reallocation module attempts to reassign team members using optimization techniques like scipy.optimize or cvxpy, or defaults to "No backup" if none are available. A reasoning engine powered by GPT-4 analyzes the project state, identifies delay causes, and suggests mitigation strategies like better communication, added resources, and task prioritization. Reassigned and delayed tasks are summarized, and a natural language prompt is generated for GPT to produce a professional project management analysis. The agent outputs insights on risk, resource efficiency, and recovery plans in clear, human-readable language. Additionally, the agent integrates memory storage (e.g., FAISS, Chroma, or SQLite) to track project decisions over time. Ultimately, this intelligent project assistant combines AI-driven planning, optimization, and reasoning to manage complex projects proactively and autonomously.

Видео How to Create an AI Agent that Reads Legislation and Summarizes Each Document канала Data Science, Machine Learning, and Python