Palantir exec: the biggest mistake retailers are making with AI? Trying to do it all with one agent

Retail and brand teams are under unprecedented pressure today and the world is shifting faster than systems can adjust. Customer expectations reset in real time, tariffs and input costs are repricing entire categories overnight, and planning assumptions that held last quarter no longer apply. 

In this environment, many executives are looking for one AI agent to read the market, interpret requests, pull the right data, apply the right business logic, forecast demand and generate decisions across the entire operation.

It sounds like the perfect answer to every challenge hitting retail right now, but it’s not. And the retailers deploying AI this way are quietly building systems designed to fail.

From Prompts to Agentic Workflows

AI, as most people understand it, is a single exchange—prompt in, answer out. Retail decisions, however, are never single exchanges, but chains of interdependent steps and moving parts. Companies make multiple seasonal buys each year, across every category they sell, and placing each one involves reading prior sell-through, checking open-to-buy budgets, applying margin targets, and committing to quantities across sizes and colorways. 

A multi-agent approach to AI keeps those steps intact rather than collapsing them into a single prompt-and-response. One agent interprets the request. A second retrieves the relevant data. The next applies the policy or business logic. And another produces the output. Each agent passes a defined output to the next, making the process explicit, auditable and controllable.

The workflow is the same, but the structure supporting it finally matches the complexity of operations.

The One Agent Problem

Think of the steps involved in a........

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