Over the past week, I’ve been experimenting with a practical agentic-AI use case inside TradingEdgeIQ (tradingedgeiq.com) based on inspiration from Lewis who has been using about 379 AI agents to run multiple businesses for/with him. I’m building a Business Operations Center where AI-supported workflows can perform recurring business tasks while remaining visible and accountable to a human operator. For example, the system can: 🔎 Research competitors and relevant industry developments 🧠 Generate a structured intelligence brief 📅 Run work according to a defined schedule 💰 Operate within per-run, daily, and monthly AI spending limits 🚨 Surface failures or questionable results for attention ✅ Route sensitive actions through human approval 📋 Preserve an audit trail showing what ran, what it produced, and what happened next One workflow has already generated a real weekly competitor-and-industry brief from beginning to end in my staging environment. The broader operating model is still being validated before it is used more extensively. The important lessons for me have been these: ✳️ An AI agent becomes much more useful when it is surrounded by an operating system, not merely given a prompt. ✳️ The model may perform the reasoning, but dependable agentic work also requires scheduling, memory, permissions, cost controls, failure recovery, human review, and evidence of what actually happened. I think the same principle applies to trading. An agent should not simply produce a trade idea and disappear. A more responsible architecture should separate: 🔎 opportunity discovery 📊 evidence analysis 🧪 strategy simulation 🧭 the final human decision I’m curious how others here are approaching this. Are you building individual AI agents, or are you building a control system that makes those agents reliable, or both?

It's interesting to find I can get market data from Yahoo Finance for free. That means I don't have to subscribe data from CME. ※ recap: Day 69: connecting live TradingView-adjacent data (MES/MNQ prices via Yahoo Finance) into your agent spec — test pull confirmed working.

I have noticed that the prompts provided are long, in the past with my claude builds they were always completed in steps or phases. perhaps that was my "claude.md " rules I gave it before realizing that is what I was doing. Question is, if we always use long prompts how we would know if claude is hallucinating and keeping on track? Waiting till completion to debug an issue? What are your thoughts on how you debug?

Please let me know your thoughts on my thoughts. On August 5th, I'm hosting a free training where I lay out all of my agents that allow me to make money again and again. My aim is to have you copy and paste my agents into your own efforts at building a business or making money. Register here - https://01accelerator.com/a2i

A promising trade can appear in seconds. However, deciding whether it deserves your capital or not... that takes more than a chart and a good-looking return. You need to ask: 🔎 What does the market context tell you? 📊 What evidence supports the strategy? ⚠️ Where is the risk concentrated? 🧪 Does the idea survive when its assumptions are tested? That thinking is the core inspiration behind TradingEdgeIQ. What began as CoinPulse is evolving into a broader platform designed to help traders: ✅ Discover opportunities ✅ Validate strategies ✅ Understand risk ✅ Test assumptions ✅ Make more informed decisions The goal is simple: Move beyond merely finding a trade—and build a decision you can stand behind. 🎥 I’ve attached a short video introducing the vision behind TradingEdgeIQ. I’d genuinely appreciate your feedback. 💬 What is the one thing you need to see before you trust a trading idea enough to act on it? Explore the vision: https://tradingedgeiq.com Research and analytics only. No financial advice. Historical and simulated results do not guarantee future performance.