LONDON — AWS has latched on firmly to the concept of frontier agents, systems that take AI agents a step beyond merely assistive tools to those that can complete complex tasks entirely autonomously. 

In a keynote at the vendor’s London Summit this week, Francessca Vasquez, vice president of professional services and agentic AI at AWS, framed these systems around three core capabilities: autonomy, scale, and persistence.

“Frontier agents are a new class of agents that are significantly more capable,” Vasquez said. “You can direct them toward a goal, and they will figure out exactly how to achieve it. They’re massively scaled, able to perform multiple concurrent tasks and capable of working for hours or even days in pursuit of ambitious and sometimes amorphous goals.”

In this landscape, AWS last year launched Kiro, an agentic development platform that independently writes code using natural language prompts. Vasquez framed the launch as addressing a widening gap in the scalability of software development tools.

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“These tools were generating code, but builders couldn’t guide the process or ensure it aligned with their team standards,” she said. “We wanted to take everything that is exciting about AI-powered software development and add the structure that our developers really need.”

AWS also showcased its DevOps Agent and Security Agent, which are used to diagnose errors and scan for vulnerabilities as software is being built.

The intention behind the updates is essentially about speed and efficiency, according to AWS

“What used to take years can now be done in days, if not minutes,” Vasquez said. 

Frontier Agents in Practice

A concrete demonstration of Kiro in action came from U.K. used car marketplace Motorway. With its teams increasingly demanding AI coding tools, the company introduced Krio as a single, centralized system that could accelerate processes without jeopardizing oversight.

To this end, Kiro generates user stories, acceptance criteria, technical design documents, and architecture diagrams before any code is written, creating a framework to guide code development in a process that would take days to execute manually. 

Ryan Cormack, principal engineer at Motorway, told AI Business that more than 80% of Motorway’s engineers are now daily users, with Kiro generating more than a million lines of code each month.

“We’re not using AI to wildly change the way that we as a software organization are working,” Cormack said. “We’re using it to just do things that we want to do quicker.”

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That speed comes with its own risk; however, Kiro is capable of writing code faster than human engineers can reliably check. 

“Again, standardizing processes became very important for us because our teams were all reviewing the Kiro-generated code differently,” Cormack added. “We made sure we have really strong engineering processes around the planning phase, and that engineers are steering Kiro through the code-writing, so we don’t lose oversight.”

That distinction is particularly relevant as concerns about AI governance grow. On this point, Cormack noted that Motorway used AWS’s shared responsibility model to maintain security, while Kiro’s inclusion of mandatory planning phases and review checkpoints helps ensure transparency at scale.

Looking ahead, Cormack says the platform’s potential is still largely untapped. 

“It’s not even a year old and we’ve already seen explosive change in the industry,” he said. “We’re just really excited to see what else it can change.”

Data for Environmental Monitoring

Hillary Tam, head of go-to-market sustainability for EMEA at AWS, told AI Business about how the vendor is also exploring data collection in environmental monitoring.

AWS has partnered with London’s Natural History Museum as part of this effort, deploying a network of sensors across the museum’s gardens in South Kensington to capture environmental data in real time. 

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Using the data, researchers can analyze how urban conditions such as rising temperatures and heavy traffic can affect biodiversity, and model potential mitigating solutions. Tam described the gardens as AWS’s “first living lab” exploring how AI can transform environmental data into insights.

“We’ve got around eight million data points now, and it’s growing by the hour,” she said. “Ultimately, we want to turn that into actionable insight for policymakers and businesses so they can make the right interventions that bring people and planet into better balance.”

Beyond the immediate application, Tam said the project illustrates a wider shift in how organizations should think about sustainability data. While a wave of European ESG reporting requirements is pushing operational data into cloud infrastructure, compliance, she argued, is just the starting point.

“Once you have that data foundation, what more can it tell you? How can you better serve your customers? Where are there opportunities for innovation?” She said, “We’re moving sustainability from a cost center to a space where new business models, and in some cases entirely new businesses, are being born.”