Amazon Web Services (AWS) today at its re:Invent 2025 conference extended its Kiro artificial intelligence (AI) tool to provide access to a set of agents that have been trained to trigger specific actions as they observe the way code is being created.
Adnan Ijaz, director of project management for Kiro at AWS, said these specialized agents, dubbed Kiro Powers, enable developers to invoke steering files that are extensions to the specifications-based approach that AWS has adopted to improve the quality of the code generated by an AI tool by narrowing the scope of the tasks that AI agents are allowed to perform.
The overall goal is to make AI agents more efficient by reducing the amount of “context rot” in a way that serves to better fine tune the outputs generated, he added. That approach ensures the AI agent doesn’t become unduly influenced by data that is outside the scope of the task it has been assigned that could result in a hallucination, he added.
AWS earlier this week also extended its AI portfolio to include a set of AI agents for optimizing DevOps workflows. AWS is now previewing an AI agent within its Kiro AI coding tool that autonomously works in the background to optimize tasks such as pull requests and provide feedback to developers. Additionally, AWS previewed an AWS Security Agent to review and test code, including conducting penetration testing, and an AWS DevOps Agent that will always be on call to help manage IT incidents.
The AI agents added to the Kiro AI coding tool are also designed to only load when needed to minimize costs. Trained using best practices developed by AWS, those agents are able to access hooks and Model Context Protocol (MCP) servers to trigger specific actions, including invoking third-party services from Datadog, Dynatrace, Postman and others, as code is being developed, said Ijaz.
For example, an AI agent might automatically invoke the Datadog monitoring service to identify an infrastructure issue that is adversely impacting the ability of the code generated by Kiro to be optimally run, he added.
In time, more specialized AI agents will be added to the AWS portfolio that will independently perform tasks in the background in real time as application developers build their code, noted Ijaz. Those capabilities in time will substantially reduce the amount of friction that software development teams currently encounter as they invoke multiple tools, he added.
Mitch Ashley, vice president and practice lead for software lifecycle engineering at Futurum Group, said AWS is providing agents the hooks they need to operate as real contributors to the development of software. When an agent can reason using telemetry data provided by, for example, Dynatrace or validate application programming interfaces (APIs) created using Postman tools, it can more fully participate in development workflows, he added.
Ultimately, AWS plans to expand the ecosystem of providers of DevOps platforms and tools that can be invoked by its AI agents. The only issue that remains to be seen now is how the role of the DevOps engineer will evolve as more of the capabilities provided by those tools are shifted further left across the software development lifecycle (SDLC).
