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Deep Dive: Multi-Agent Trading Systems — When Models Learn to “Pass the Buck,” Does Decision Quality Go Up or Down?
Trading
JIN13 min read·Just now--
Disclosure: I use GPT search to collection facts. The entire article is drafted by me.
This isn’t just a bunch of LLMs arguing about stocks.
What multi-agent trading architectures actually do — when built correctly — is transform “cognitive labor” into an observable, auditable, reversible engineering pipeline. Whether that transformation improves outcomes or just introduces more failure modes is the honest question this article tries to answer.
The Problem With the Single Oracle
Ask a single agent: “Should I buy NVDA right now?” It will synthesize the most authoritative-sounding data it can find and output a confident recommendation.
Ask it again with a different framing: “What are the risks in buying NVDA right now?” It will flip completely and give you an equally confident bear case.
This isn’t a model failure. It’s a structural failure. A single agent optimizes for coherence within a prompt — not for rigor across contradictory evidence. It’s an excellent sophist. And in financial decision-making…
