ATLAS Runs Self-Improving Market Agents

This is less a chatbot demo than a feedback loop for turning market research into an evolving system.

• –25+ agents span macro, sector, superinvestor, and decision layers, so the system is structured like a research desk rather than a single prompt chain.
• –Prompt rewrites are treated like model updates: bad performers get adjusted, then kept or reverted based on outcome, which is a neat software-engineering analogue to training.
• –The Sharpe-based weighting scheme is the strongest idea here, but it also invites regime overfitting if the market context shifts faster than the agent loop can adapt.
• –The live-capital claim makes this more interesting than a backtest toy, because the real test is whether the governance holds up under messy market drift.
• –For AI builders, the takeaway is the architecture pattern: detect recurring blind spots, spawn specialists, and use scored outcomes to prune the system over time.