Harness this week extended its alliance with Amazon Web Services (AWS) to integrate their respective artificial intelligence (AI) agents to automate DevOps workflows.
Announced at the AWS re:Invent 2025 conference, Harness is now integrating the Model Context Protocol (MCP) server it added to its continuous integration/continuous delivery (CI/CD) platform with the Kiro AI coding tool developed by AWS.
At the same time, Harness is now making its entire DevOps platform available on the AWS cloud.
Bala Venkatrao, senior vice president and general manager for platform at Harness, said that integration will make it possible for the AI agents that are being embedded into Kiro to invoke AI agents that Harness has developed to automate workflows using a Harness Software Delivery Knowledge Graph that helps ensure more accurate outputs are generated.
That approach will enable developers to invoke, for example, security scans as they code, with DevOps teams then relying on Harness to automate workflows after code is checked in.
Longer term, Harness expects to develop similar relationships with other providers of AI coding tools, noted Venkatrao. The Harness CI/CD platform will, in time, become the vehicle for integrating and observing DevOps workflows spanning multiple cloud computing environments, he added.
In the meantime, the deeper ties with AWS align with a broader effort to automate DevOps workflows using AI agents. The cloud service provider is previewing an AWS Security Agent to review and test code and an AWS DevOps Agent that will always be on call to help manage IT incidents.
Mitch Ashley, vice president and practice lead for software lifecycle engineering for The Futurum Group, said the Harness integration with AWS demonstrates how AI-generated code and delivery systems can operate as one workflow. Kiro gains direct access to the delivery context, and Harness brings the intelligence needed to validate, secure, and move that code through pipelines without slowing teams down. Developers stay inside their environment while agents see the full picture of build health, policy checks, and production readiness, he added.
As a result, Harness is positioning delivery as an adaptive system that learns from every build and release, and AWS provides the platform where that automation can run at scale. As this approach takes hold, AI software development will shift from being a set of disconnected stages to a continuous operational loop that improves with each change, noted Ashley.
Neha Goswami, director of engineering for Amazon Q Developer at AWS, said the overall goal is to reduce the level of friction software engineering teams currently experience between when code is checked in and when an application is deployed in a production environment. AWS does not have any ambitions toward building its own CI/CD platform or code repository, but there is an opportunity to leverage AI agents to streamline DevOps workflows.
In fact, as more code is generated using AI coding tools it will soon become apparent that every organization that builds software will need a CI/CD platform to manage those workflows, noted Goswami.
It’s still too early to say how best DevOps practices will evolve in the age of AI but the one thing that is certain is AI agents will make it simpler for application developers to invoke a range of backend services that should improve the quality of the code being created before it finds its way into a build.
