An agentic commerce agent works by starting with a user request, or prompt. Unlike traditional AI systems, which give static, one-off responses, modern agents are built to interpret these requests, consider context, adapt on the fly and decide how to move forward.
The input can also start a conversation to kick-start a goal-oriented action. For instance, if you make a very broad request like “I need a new shirt,” the AI retail agent can reply asking for more specifics, such as whether you’d like a pattern or a certain type of fabric, or for what kind of occasion the shirt would be worn.
Agents then automate research and product discovery. Instead of simply searching a single website, the agent can search across multiple e-commerce platforms, access and analyze product specifications, reviews and ratings, compare prices in real time and evaluate shipping times, return policies and other logistical details.
From there, the agent doesn’t just present a list of options, but can actively reason through them based on the user’s initial parameters and its own understanding of what constitutes a good deal or best fit.
