In a sea of agents, AWS bets on structured adherence and spec fidelity

Despite new methods emerging, enterprises continue to turn to autonomous coding agents and code generation platforms. The competition to keep developers working on their platforms, coming from tech companies, has also heated up.

AWS thinks its offering, Kiro, and new capabilities to ensure behavioral adherence set up a large differentiator in the increasingly crowded coding agent space. 

Kiro, first launched in July on public preview, is now generally available with new features, including property-based testing for behavior and a command-line interface (CLI) capability to tailor custom agents.

Deepak Singh, AWS vice president for databases and AI, told VentureBeat in an interview that Kiro “keeps the fun” of coding while providing it structure.

“The way I like to say it is, what Kiro does is it allows you to talk to your agent and work with your agent to build software just like you would do with any other agent,” Singh said. “But what Kiro does is it brings this structured way of writing that software, which we call spectrum and development, to specs that take your ideas, converts them into things that will endure over time. So the outcome is more robust, maintainable code.”

Kiro is an agentic coding tool built into developer IDEs to help create agents and applications from prototype to production.

In addition to new features, AWS is offering startups in most countries one year of free credits to Kiro Pro+ and expanded access to Teams. 

Behavioral adherence and checkpointing built in

One of the new features of Kiro is property-based testing and checkpointing. 

A problem some enterprises face with AI-generated code is that it can sometimes be difficult to judge accuracy and how closely the agents adhere to their intended purpose. AWS noted in a blog post that “whoever writes the tests (human or AI) is limited by their own biases— they have to think of all the different, specific scenarios to test the code against, and they’ll miss edge cases they didn’t think of. AI models often ‘game’ the solution by modifying tests instead of fixing code.”

“What property-based testing does is it takes a specification, it takes a spec, and from that, it identifies properties your code should have, and it basically creates potentially hundreds of testing scenarios to verify that your code is doing what you intended it to as identified in the spec, and it does all the automatically,” Singh said. 

Singh said that organizations can upload their specifications, and the Kiro agent can start identifying what is missing, even before the code review process begins. 

Property-based testing matches the specified behavior, aka your instructions, to what the code is doing. Kiro can help users write it in their specifications based on the EARS format. For example, if a company is building a car sales app, the specification would read:

“For any user and any car listing, WHEN the user adds the car to favorites, THE System SHALL display that car in their favorites list. PBT then automatically tests this with User A adding Car #1, User B adding Car #500, User C adding multiple cars, users with special characters in usernames, cars with various statuses (new, used, certified), and hundreds more combinations, catching edge cases and verifying that implementation matches your intent.”

As opposed to a traditional unit test specification, which states: If a user adds car #5 to their favorites, then it will appear on their list.

Kiro will then identify examples of the code violating the specifications and present them to the user. 

Kiro also now allows for checkpointing, so developers can go back to a previous change if something goes wrong. 

CLI coding

The second major new feature of Kiro is Kiro CLI, which brings the Kiro coding agent directly into a developer’s CLI.

AWS said the Kiro CLI utilizes some functionalities from the Q Developer CLI—its in-line coding assistant, launched in October 2024—to enable users to access the agent from the command line. 

It also allows developers to start building custom agents, such as a backend specialist, a frontend agent, and a DevOps agent, tailored to an organization’s codebase.

Singh said developers have their own unique ways of working, so it’s important for coding agent providers like AWS to meet them, where they are. Kiro CLI allows users to:

• Stay in the terminal without the need for context switching

• Structuring AI workflows with custom agents

• Have one set up for two environments since MCP servers and other tools work in both the Kiro version on the IDE or the CLI

• Fast automation to format code or manage logs through automated commands

Coding agents competition

Kiro, though, is just one of many coding agent platforms cropping up and competing for enterprise usage. 

From OpenAI’s GPT-Codex, which unifies its Codex coding assistant with IDEs, CLIs, and other workflows, to Google’s Gemini CLI, it's clear that more developers demand easy access to coding agents where they do their work. 

And enterprises are demanding more from coding agents. For example, Anthropic made its Claude Code platform available on the web and mobile. Some coding platforms also allow users to choose which model to use for their coding. 

Singh said Kiro doesn’t rely on just one LLM; instead, it routes to the best model for the work, including AWS models. At launch in July, Kiro was based on Claude Sonnet 3.7 and 4.0. 

Well-known brands like Monday.com have noted the significant benefits of AI-powered coding, demonstrating that enterprises will likely continue to utilize these platforms in the future. 

“We saw that the mental model changes for developers, but it’s not just about becoming more efficient; it’s also how they organize around the way they work now,” Singh said. 

Original Source: https://venturebeat.com/ai/in-a-sea-of-agents-aws-bets-on-structured-adherence-and-spec-fidelity