What Is Agentic Commerce Optimization? How Brands Can Get Recommended By AI

Max Sinclair is the CEO and Co-Founder of leading Agentic Commerce Optimisation and AI Visibility Startup Azoma.ai.

E-commerce brands have long optimized for search rankings, marketplace placement and paid media.

Now, another layer is influencing what consumers see and buy: AI agents.

When a shopper asks Alexa for Shopping, or when they use Walmart Sparky, ChatGPT or Gemini for the best product for a particular need, the system is doing more than retrieving a list. It is interpreting the question, assessing products against the request, weighing evidence from different sources and deciding which options it can confidently recommend.

That creates a new optimization problem for brands. Agentic commerce optimization (ACO) is the process of improving how products are discovered, understood, evaluated and recommended by AI shopping agents.

As the CEO and co-founder of Azoma, where we help leading brands with ACO and generative engine optimization (GEO), we analyze millions of AI shopping interactions to understand what influences product recommendations.

While ACO builds on search engine optimization (SEO) and answer engine optimization (AEO) principles, I’ve found many brands still struggle with applying the lessons from those disciplines to systems that can interpret intent and make recommendations.

From Search Rankings To Recommendations

Traditional e-commerce gives brands a clear objective: improve rankings, increase visibility and drive shoppers to a product page.

AI agents compress that journey. A shopper might ask, “What is the best moisturizer for sensitive skin?” The agent has to interpret the requirements, understand the products available, determine which products fit and construct a recommendation.

ACO brings together the work required to make a product understandable, relevant, supported by credible sources and accurately represented.