Amazon Web Services (AWS) today with the general availability of its Kiro artificial intelligence (AI) coding tool added additional capabilities, including a version that can now be invoked via a command line interface (CLI).
At the same time, Kiro now enables property-based testing (PBT) to measure whether the code generated matches the behavior defined in the original specification.
Kiro now also provides the ability to rewind to a previous checkpoint the tool creates, enabling developers to rollback changes in addition to allowing Kiro to work across multiple project roots simultaneously.
Finally, AWS announced that it is giving away one year’s worth of Kiro Pro+ for qualifying startups that sign up by the end of this year.
David Yanacek, a senior principal engineer for agentic AI at AWS, said these capabilities collectively will make it simpler for developers to generate higher-quality code using an approach based on specifications that focus the AI model on a narrow range of tasks to ensure better outputs. That output can now also be tested using a set of properties that the tool creates based on those initial specifications.
PBT is designed to probe code using a technique called “shrinking” that functions almost like a red team trying to break your code. When it finds violations or counter-examples of better code, Kiro can automatically update your implementation, or surface options for you to fix the original specification, the code created or, if necessary, the PBT itself.
The overall goal is to produce more secure code that is easily understood by application developers that might need to debug it, he added. That approach provides the added benefit of reducing stress on DevOps engineers that might otherwise be created using AI coding tools that don’t rely on specification and PBT to generate better code, noted Yanacek.
Additionally, application development teams that opt for the CLI version of Kiro can make use of custom AI agents they have created to automate specific tasks versus relying on the steering files embedded with the AI coding tool, he added.
Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said CLI support in Kiro reinforces how important it is to have development tools that fit cleanly into the AWS ecosystem. Developers need an IDE and terminal workflow that share the same agents, steering files, and security settings so they can move between environments without losing context. The added capabilities in this release move Kiro beyond code generation into disciplined, verifiable development to provide a more dependable path for organizations that want AI powered development to scale with quality, not just speed.
There is, of course, no shortage of AI coding tools. AWS, however, is making a case for a more structured approach that walks developers first through requirements and visual designs to generate a specification. That approach enables developers to keep AI models focused on a specific task versus more arbitrarily adding steps that are beyond the scope of the task assigned, noted Yanacek.
That capability is critical because otherwise application developers will spend a significant amount of time creating their own prompt and context engineering workflows to achieve the same outcome, he added.
Alternatively, application developers can also revert to more of vibecoding technique if, for example, all they need to create is a simple script, noted Yanacek.
It’s not clear how much code is being generated using AI or, more importantly, how much of that code is considered good enough to use in a production environment. The one thing that is for certain is that without the proper guardrails in place AI coding tools quickly become too much of a good thing that may do more to reduce productivity than enhance it.
