In case you haven’t noticed, how we search for anything online is quietly undergoing a transformation – not just on Google, but also e-commerce websites during online shopping. Traditionally, e-commerce search has been a structured, filter-heavy exercise.
But Amazon believes the next phase of online discovery will look far more conversational – and far more intelligent. At the centre of that shift is Rufus, Amazon’s conversational shopping assistant, built around what the company calls an agentic AI architecture.
As Rajeev Rastogi, Vice President, Machine Learning at Amazon, explains, the system isn’t replacing traditional search – it’s orchestrating it better than before. “The conversational shopping assistant, Rufus, actually brings the best of both worlds. The previous search paradigm of searching over billions of products – which is a non-trivial task – and the way agentic AI works is that these agents have access to tools. They take in a natural language request and they create a plan of action, and they use reasoning just like humans do to decide what tools they would use to respond to the user.”
In practice, this means Rufus behaves less like a search box and more like a research assistant. Ask it for the best smartphone for recording Instagram Reels or YouTube videos under ₹20,000, and it doesn’t just look for matching keywords. Instead, it reasons through the request – identifying relevant specifications, querying product databases and synthesising results.
Behind the scenes, Amazon’s long-developed product search engine still has an important role to play. “The old search paradigm is still around, except that it gets buried as a tool, and Rufus is sort of like the layer that does the natural language interactions and serves as the interface,” according to Rastogi.
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That hybrid architecture is deliberate. Large language models are powerful, but they’re not perfect, highlights Rastogi. If they’re left alone, they can hallucinate facts or produce misleading answers. Hence, Amazon’s approach is to ground the AI in specialised systems that already perform certain tasks extremely well.
“You reduce hallucination that way. Because when you use your own search engine also, you are going over your own products, and the search engine we built over many years is optimized,” emphasizes Rastogi.
More broadly, he argues that the future of AI systems will depend on this kind of collaboration between language models and specialised tools.
“LLMs can’t solve everything. They can’t solve optimization problems. There will be specialized engines and tools which are very good at that one task… and LLMs will use those tools, and that’s where agenting AI comes in.”
The improvements in reasoning capability are what make that orchestration possible. Interestingly, the same data-driven philosophy powering Rufus also shows up in less visible parts of Amazon’s operations – including something as mundane as packaging.
“We have massive amounts of data over products, and we can pretty much predict the probability of damage if a product went in a certain package type. Therefore, you select packaging so that you keep the damage probability below a threshold.”
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AI is also being deployed on the seller side of the marketplace, particularly to help small merchants create better listings without needing professional photography or marketing teams, Rastogi points out. “You just upload an image and a few keywords, and then we use Gen AI to populate a lot of the attribute values. The seller doesn’t have to manually fill in everything.”
Rastogi highlights how GenAI can even transform product images themselves. “We can take non-white background images and put a white background, or even generate realistic backgrounds with shadows… so it actually looks like a studio shoot.”
Yet as agentic systems become more powerful, a larger question becomes difficult to ignore. How do you ensure they remain aligned with customer interests? For Amazon, Rastogi explains, the answer lies in the instructions given to the AI itself.
“The most important thing in agentic AI and GenAI is the prompt instructions you give to the agent. You create a constitution which says you will never prioritize profit over what’s the right thing for the customer,” Rastogi underlines.
What this means is that the future of e-commerce may not just depend on smarter algorithms, but also on the rules we give them.
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