Beyond LLMs: What it takes to build reliable infrastructure for agentic trading
By Ramakant Yadav, Founder, Scalar Field
Every few weeks, a new AI model tops a leaderboard, and the industry declares that AI agents have arrived. But when those agents are trusted with real capital, a less glamorous reality becomes clear: the model is only a small part of the system. In agentic trading, the language model may represent perhaps a tenth of the engineering challenge. The other 90 per cent lies in infrastructure — execution, risk controls, state management, reconciliation and auditability. These are not the technologies that dominate AI headlines, but they determine whether an autonomous system can actually be trusted with money.
Agentic trading represents a significant shift from conventional investment technology. A person can express a market belief in plain language, while an agent researches the idea, tests whether it has worked historically and, within an approved mandate, executes and manages positions on live markets. Once real...
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