Kunal Ghosh

Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) with Amazon Bedrock AgentCore and coding agents like Kiro often struggle with the gap between conceptual frameworks and working code. Amazon Bedrock AgentCore is a service for building, connecting, and optimizing agents at scale with any framework or model. AI-DLC positions AI as a central collaborator across the software development lifecycle, handling routine execution while humans retain oversight of critical decisions.

Organizations often deploy agents using Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. These agents may access governed knowledge bases hosted in separate AWS accounts. This cross-account separation helps maintain clear workload boundaries but can introduce integration challenges.

If you manage data in Amazon Aurora MySQL-Compatible Edition and want to make it available for analytics, machine learning (ML), or cross-service querying in a modern lakehouse format, you’re not alone. Organizations often need to run analytics, build ML models, or join data across multiple sources. These are examples of workloads that can be resource-intensive and impractical to run directly against a transactional database.