“Clearly the entire operating layer is becoming agentic,” said Rajeev Rastogi, Vice President of Machine Learning at Amazon, pointing to a decisive transformation in business technology.

This marks a transition from static software to autonomous systems capable of understanding natural language instructions. Unlike conventional software that follows rigid, pre-programmed scripts, agentic systems possess dynamic execution paths.

“Agents prepare plans dynamically to solve a problem... and invoke the appropriate tools,” Rastogi explained during a fireside chat at the 17th edition of YourStory’s flagship tech-startup event, TechSparks.

Instead of relying on human intervention at every step, these autonomous agents evaluate queries, formulate a sequence of actions, and call specialised digital tools to complete complex tasks end-to-end.

Within Amazon, this operating layer is active in systems like Rufus, a conversational shopping assistant that parses customer requests, applies price filters, invokes search engines, and guides users through product comparisons and checkout journeys. Similarly, autonomous seller assistants pre-fill product listings, monitor inventory levels, and guide small businesses through international export compliance.

Despite intense market enthusiasm surrounding generative artificial intelligence (Gen AI), Rastogi emphasised that generative models are not a universal cure for every computing challenge. Generative AI excels at complex reasoning, text summarisation, review analysis, and creative tasks such as editing product image backgrounds. However, it struggles with high-volume, low-latency applications.

“For certain applications, Gen AI models will never match the performance that you can get from traditional machine learning models,” Rastogi pointed out.

Traditional machine learning relies on classical algorithms such as gradient-boosted decision trees or standard neural networks that process structured numerical data with extreme speed.

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