I want a single, streamlined solution that continuously scrapes live data, feeds it straight into an AI agent, and immediately turns the results into usable insights and automated replies. Here’s the flow I’m aiming for: the scraper pulls fresh information from the sources I’ll share with you, filters and structures it on the fly, then hands it to the agent. The agent should run on-the-spot data analysis and generate context-aware responses without human intervention. Think Python with Scrapy or Selenium for the collection layer, fast in-memory handling (Redis, Kafka, or similar) to keep everything real-time, and an LLM framework such as LangChain or a custom GPT wrapper for the reasoning layer. If you have a better tech stack in mind, I’m open to it as long as it remains fast and easy to scale. Deliverables I need to sign off on: • A working scraper that operates in real time and can be pointed at additional sources with minimal code changes. • An AI agent that ingests the incoming stream, performs on-demand data analysis, and returns automated responses through a simple API endpoint. • Clear instructions (env vars, setup, run scripts) so I can launch the entire pipeline on my own server or cloud account. I’ll test the build by pointing it to live data, checking that latency stays low, and verifying that the agent’s outputs match the incoming information. Once those checks pass, we’re done.