What happened
Engineering teams that adopt the AI-Driven Development Lifecycle (AI-DLC) often find it difficult to move from conceptual ideas to functional code, according to a new AWS Machine Learning post.
The post highlights two reference implementations built with Amazon Bedrock AgentCore, Kiro, and Claude Code. One is an SQL-to-ER-diagram generator, and the other is a multi-agent code security analyzer.
Both implementations are presented as practical examples of the AI-DLC construction phase, showing how the workflow can be applied in real scenarios.
Why it matters
The gap between concept and working code is a common sticking point in AI-assisted development. These examples provide concrete patterns that teams can study or adapt.
By focusing on the construction phase, the post addresses a specific, hands-on part of the AI-DLC process that is often harder to grasp than planning or testing.
Using tools like Amazon Bedrock AgentCore, Kiro, and Claude Code, the implementations may help engineering teams understand how to structure multi-agent workflows for their own projects.
Key facts
Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) often struggle to turn concepts into working code.
The AWS Machine Learning post walks through two reference implementations.
The reference implementations use Amazon Bedrock AgentCore, Kiro, and Claude Code.
The two implementations are an SQL-to-ER-diagram generator and a multi-agent code security analyzer.
The implementations put the AI-DLC construction phase into practice.
What to watch next
Teams exploring AI-DLC may look to these reference implementations as templates for their own construction-phase challenges.
It will be interesting to see whether AWS expands on these examples with additional use cases or deeper guidance on building agents with AgentCore.
The role of multi-agent setups in code security and database design could inspire similar projects in other parts of the development lifecycle.