AI Trading Agents: Promise vs. Practice in 2026

As AI frameworks make sophisticated trading systems accessible to individual developers, impressive backtest results create a false sense of security. The critical gap between simulated performance and real-world execution—driven by transaction costs, market dynamics, and liquidity constraints—remains largely unaddressed. This matters because it reflects a broader challenge in AI deployment: raw technical sophistication doesn't guarantee practical value in cost-sensitive, dynamic domains like finance.