Building a Production-Ready Agentic Fraud Detection System — Part 1: The Full Picture

A new production-ready agentic fraud detection system uses three cooperating AI agents to score transactions from independent signals, orchestrated by a LangGraph workflow with a human-in-the-loop pause, guardrails, and LangSmith tracing, deployed on AWS with a FastAPI backend and Streamlit frontend, as documented in a 4-part series by developer nitsourish on GitHub.

Member-only story Building a Production-Ready Agentic Fraud Detection System — Part 1: The Full Picture This is Part 1 of a 4-part series: Build → Harden → Observe → Ship. This post covers the full system — every component, how they connect, and why. Parts 2–4 go deep on security/guardrails, MCP + LangSmith observability, and the AWS deployment, respectively. Most fraud-detection write-ups end at a confusion matrix. A model gets trained, ROC-AUC gets reported, and the story stops right where the interesting part begins: what happens when that score has to become a decision , in real time, inside a system a human can trust, audit, and interrupt. This series documents a system built to answer that question properly — not a notebook, a running service. Three cooperating agents score a transaction from independent signals, a LangGraph workflow orchestrates them with a genuine human-in-the-loop pause not a spinner , every stage is wrapped in guardrails, every run is traced in LangSmith, and the whole thing is containerized and deployed on AWS behind a FastAPI backend with a Streamlit frontend. Repo: github.com/nitsourish/Agentic-AI-Orchestration — -Real-Time-Transaction-Fraud-Detection https://github.com/nitsourish/Agentic-AI-Orchestration---Real-Time-Transaction-Fraud-Detection.git The two architecture diagrams referenced throughout this post live in the repo at architechture/architecture.html full system and…