Agentic trading mandates vs classic EA portfolios

The shift from classic EA farms to agentic workflows is less about performance and more about how you handle operational overhead. In a traditional setup like EA Farm, you manage a collection of discrete, rules-based scripts. You typically monitor each one's performance, manually adjust risk parameters, and handle rebalancing yourself. It works fine until the number of strategies scales beyond your ability to watch them.

Agentic frameworks, such as QuantMogul Agentic Trading, introduce a layer of abstraction. Instead of tweaking individual EAs, you define a high-level mandate. The agent then dynamically adjusts allocation or rebalances based on your written risk rules. It is essentially an automation layer that sits on top of your execution logic, trying to enforce consistency across a portfolio.

This approach has clear benefits for discipline:

• Automated enforcement of stop-loss and drawdown limits.
• Natural language prompts for quick portfolio rebalancing.
• Reduced manual oversight for routine maintenance.

However, agents have blind spots that can be dangerous. They often suffer from correlation blindness, failing to see how different EAs might react to the same volatility spike until it is too late. An agent is only as good as the guardrails you code into it. If you treat it like a 'set and forget' system, you will eventually get burned by a systemic event that falls outside your defined rules. You still need a human veto to handle edge cases that the agent logic doesn't cover. Do you trust an LLM-based agent to handle your risk management, or does the lack of transparency in its decision-making process keep you sticking to static, deterministic EAs?