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Developer workflow poised for change as AWS advances continuous-learning AI agents
For all the hype around artificial intelligence coding assistants, many treat every session as a blank slate — no memory of yesterday’s preferences, context or institutional knowledge. That’s the gap agentic AI aims to close, not just helping developers write code but functioning as persistent, context-aware teammates.
Developers typically spend only 20% to 30% of their time writing code, with the rest consumed by DevOps, on-call rotations, security and operational overhead, according to Swami Sivasubramanian (pictured), vice president of agentic AI at Amazon Web Services Inc. AWS’ Frontier agents are designed to absorb that toil as agentic teammates that learn codebases and build context over time.
“The problem right now with most AI assistants [is] that they always act more like an intern instead of a very tenured teammate within the company,” Sivasubramanian said. “That means every day is like day one and you keep training the intern.”
Sivasubramanian spoke with theCUBE’s John Furrier during theCUBE’s coverage of AWS re:Invent. TheCUBE, SiliconANGLE Media’s livestreaming studio, explored how agentic AI aims to reshape software development through intelligent, memory-equipped teammates.
Agentic AI promises tenfold gains through continuous learning
The productivity gains from AWS’ approach are exponential, not incremental. Where traditional AI tools promise 20% to 30% improvements, AWS is targeting five- to tenfold gains through agents that handle autonomous planning, DevOps and security testing, according to Sivasubramanian.
“Every app nowadays has a compute storage database and some AI backed in,” he said. “But every app by next year is going to be agentic, and that is going to be a given in my mind.”
The shift extends to how these systems learn at scale. When AWS runs upgrades across thousands of repositories, insights from early migrations inform later ones, allowing agents to improve continuously, according to Sivasubramanian.
“When we are upgrading 100,000 repos, then suddenly we can learn what worked in one repo and what failed and then we can apply it in a different context on … let’s say the 1,000 and first repository,” he said. “That means this agent is continuously learning. It gets better fast, really quick after [the] first time.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent:
Photo: SiliconANGLE
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