I’m putting together an end-to-end agentic AI system that will take a repetitive text-based workflow off my plate and run it automatically. The core of the build will combine an LLM with Retrieval-Augmented Generation, orchestrated through LangGraph and exposed through a FastAPI service that talks to a persistent database. My main objective is simple: automate a very specific task that currently consumes manual effort, using only text data as both input and output. Here’s what I already have in mind: • FastAPI should serve as the public interface, handling requests and routing them through the agent pipeline. • LangGraph (or comparable MCP-style orchestration) will manage the multi-step reasoning and tool usage required by the agent. • The RAG layer needs solid chunking, embedding generation and vector search so the model can ground its answers in my private corpus. • A lightweight, scalable database must store conversations, documents and metadata for easy retrieval and future analytics. I’ll provide access to my text datasets plus any API keys you need for proprietary models or open-source checkpoints. I’m happy to brainstorm the exact task flow with you, but I expect clean, well-documented Python code, Dockerised deployment instructions and a short readme summarising architecture, setup and usage. Acceptance will be based on: 1. The FastAPI endpoint completing the task end-to-end with no human intervention. 2. Accurate grounding of outputs in the supplied text corpus (measured through spot checks). 3. Clear, reproducible setup following the provided documentation. If you’ve shipped similar agentic or RAG-heavy projects before, I’d love to see a quick demo or repo link. Let’s get this automated.