Palantir executive warns retailers’ top AI error is relying on a single agent

(SeaPRwire) –   Retail and brand teams are facing unprecedented pressure right now, as global changes are occurring faster than existing systems can adapt. Consumer expectations shift in real time, tariffs and raw material costs are revaluing entire product categories overnight, and planning assumptions that were valid just last quarter are no longer relevant.

Against this backdrop, many executives are searching for a single AI agent that can analyze market conditions, interpret requests, pull relevant data, apply appropriate business logic, forecast demand and generate decisions across the entire scope of operations.

This may sound like the perfect solution to every challenge currently hitting the retail sector, but it is not. Retailers rolling out AI in this manner are inadvertently building systems that are set up to fail.

Moving Beyond Basic Prompts to Agentic Workflows

For most people, AI operates as a single exchange: you enter a prompt, you get a response. Retail decisions, however, are never isolated interactions, but sequences of interdependent steps and variable components. Companies place multiple seasonal purchase orders each year across every product category they sell, and finalizing each order requires reviewing past sell-through rates, verifying open-to-buy budgets, applying margin targets, and locking in order quantities across all sizes and color variations.

A multi-agent approach to AI preserves all these separate steps instead of condensing them into a single prompt-and-response cycle. One agent interprets the incoming request, a second retrieves relevant supporting data, the next applies relevant company policies or business logic, and another generates the final output. Each agent passes a clearly defined deliverable to the next in line, making the entire process transparent, auditable and easy to govern.

The end-to-end workflow remains unchanged, but the underlying structure now finally aligns with the actual complexity of retail operations.

The Pitfalls of Relying on a Single Agent

Consider the steps involved in processing a product return. These include understanding the customer’s request, matching it to the corresponding order, applying the correct policy and then drafting a response for the customer. When a single AI agent handles all of these steps, they are merged into one single output.

But what happens if the initial request is misinterpreted? The entire system then operates based on that foundational error. A return request may be categorized as a billing issue, the wrong policy is referenced, and the customer receives a response that sounds correct on the surface but is entirely inaccurate.

When using a single agent, workflows typically degrade in three predictable ways: errors compound because there are no checkpoints between steps to catch issues, transparency disappears because there is no audit trail showing how the output was produced, and flexibility suffers because every new task is layered onto the same existing process. With a single agent, it is far harder to pinpoint exactly where an error occurred, and one small mistake can easily cascade through the entire workflow.

The Fashion Demand Forecasting Challenge

The fashion sector is an excellent use case for a multi-agent approach, as it is an industry built entirely on predicting future consumer demand. Teams commit to orders for specific sizes, colors, fabrics and quantities months before products reach shelves. Yet in 2023, the industry produced an estimated 2.5–5 billion units of excess stock, translating to roughly $70–$140 billion in losses, a figure that highlights just how difficult accurate demand forecasting is for the sector.

Improving these forecasting decisions requires multiple layers of analysis, including reviewing performance of past collections, identifying which product attributes drove sales, linking those attributes to actual sell-through rates, and comparing those insights to current real-time demand signals.

A single AI agent tasked with “forecasting demand” would need to complete all of this work in one single pass. But just as no retailer or brand executive would ask a single planner to conduct trend analysis, historical performance reporting, demand planning and competitive research all at the same time, no retailer should expect a single agent to do so either — at least not with the level of precision, attention to detail and quality that today’s consumers demand.

A multi-agent approach distributes this work across specialized tools: the first agent scans product images from prior seasons and tags items by size, color, material and print. The next agent converts those tags into structured, actionable data that purchasing teams can actually use. A third agent maps that data against sell-through rates, markdown schedules and regional performance metrics. A fourth agent cross-references these performance patterns with current search trends, social media signals and competitor product assortments.

Each agent is responsible for a narrow, specific task and generates an output that feeds directly into the next step. The end result is not a single standalone answer, but a structured breakdown of the decision context, enabling human teams to navigate levels of complexity that would otherwise be unmanageable.

Design Workflows First, Then Build Agents

Most failed AI deployments are not caused by flaws in the underlying model itself, but by gaps at the handoff points between different steps, so teams should build a separate agent for each distinct task. Retail teams looking to build effective agentic systems should first analyze every component of their existing workflow, asking questions like “Where does the work split into separate steps?” “At what points do errors typically enter the process?” and “Where do human teams need to have visibility or decision-making control?”

These are the natural points where retailers should integrate AI agents. They should also ensure a clear output and handoff process at each step, and build in formal checkpoints where a human can review, adjust or redirect the process before the workflow moves forward.

Prioritize a Cohesive Data Strategy

Retail and brand leaders should also put in place a clear data strategy to support their agentic workflows, as siloed data is one of the biggest barriers to effective AI deployment in retail. Companies need to ensure each individual AI agent generates data that is compatible and usable by all other agents in the system. Planning outputs feed into purchasing processes, purchasing data feeds into merchandising, and this flow continues through inventory management, logistics, sales and customer service. Especially in retail, each agent needs to function as a connected link in a robust, unbroken data chain.

To build strong, reliable agentic workflows, retailers and brands should start with a specific, defined business challenge, split it into its core component tasks, create a specialized agent for each task, and build in dedicated checkpoints where human teams can review, validate and reverse decisions if needed. This approach reduces the risk that a single point of failure will disrupt an entire system, and sets appropriate AI boundaries that keep human intelligence at the center of high-stakes business decisions.

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