Just got out of a meeting with the founder of a Law Research Paper Agency. He wanted an AI Agent that could replace his interns who write Research Papers. His clients are Law Students, and he helps them write Research Papers. This was his requirement: A 10-stage "Agentic" legal factory that automates the entire lifecycle of a 5,000-word research paper. Instead of one AI, it’s a team of specialised agents: an Architect to plan the thesis, a Research Engine that pulls from "hard" legal databases (SCC Online, JSTOR, etc.), and a Citation Manager to handle 100% accurate Bluebook/OSCOLA footnotes. The system then passes the work through Academic Structuring and Formatting agents to meet strict university rules (margins, fonts, word counts). Finally, it runs through a Quality Control loop—using Turnitin for plagiarism, an "AI-Humaniser" to bypass detection, and a Final Validator that double-checks every single case cited against the original court PDF to ensure no hallucinations. Link to his Exact message: https://docs.google.com/document/d/1oMKu8Z0tOCdxR4jiyf8yNtbgHjHbaMIO39J11zIZGMg/edit?usp=sharing At first, I was like this seems complex but I guess it can be done. But after I did a lot of research on this process, it seemed sooo complex and difficult. Might be because I am a beginner, or if what he asks is even possible? I'm not sure. The current problems I face are: 1. Data ( How do I access lots of Law Judgements ) - There is only one API endpoint for Indian Law, and the rest do I have to manually retrieve the data and upload it into AWS S3 2. He wants a plagiarism checker, anda humaniserr built it for this Judgements come in every week, and the data needs to be updated. 3. He also wanted a workflow that automatically downloads all the latest judgments passed by courts and directly incorporate it into our database. (which seemed impossible to me)

Goal: Build your first "Data Pipeline" and learn to read JSON. Here’s what I successfully implemented in my first session: - Environment Setup: Successfully installed and configured a local instance of n8n to use as my automation sandbox. - API Data Fetching: Configured an HTTP Request Node (GET method) to connect to an external REST API and successfully retrieved a raw JSON dataset of user information. - JSON Manipulation: Navigated the raw JSON structure to identify specific data keys. - Expression Logic: Created a static field (Member_Status) to tag incoming data. - Execution & Debugging: Successfully ran the workflow and verified the output in the n8n execution panel, ensuring data integrity across 10 separate items.