AI-driven development lifecycle using Amazon Bedrock AgentCore
Amazon Web Services Arghya Banerjee ● Covered by 3 sources
AWS showed two Bedrock AgentCore reference apps: one turns SQL schemas into ER diagrams, the other scans code for security issues. It’s a concrete bridge between AI plans and working workflows, not just slides.
Based on reporting by Amazon Web Services, Arghya Banerjee — read the original for the full story.
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AWS is trying to make AI-DLC less theoretical and more shippable. In a new set of reference implementations, it shows how Amazon Bedrock AgentCore, Kiro, and local coding tools can sit inside real development flows instead of hovering above them as diagramware. The point is simple: AI can do the routine parts, but people still decide what matters.
The first sample takes SQL schema files and turns them into Mermaid ER diagrams. A file landing in Amazon S3 kicks off an AWS Lambda trigger, which calls an AgentCore runtime agent. That agent parses only schema metadata — tables, constraints, foreign keys — and writes the resulting .mmd file back to S3. AWS says the design is meant to keep documentation from drifting when schemas change through pull requests, while also avoiding the toil of drawing diagrams by hand.
There’s some practical engineering behind it. The agent runs in a containerized Strands setup, uses Claude Sonnet 4 through Amazon Bedrock, and keeps session context in AgentCore memory with a 90-day expiry. Large SQL files are chunked, analyzed separately, and then stitched back together so the system can cope with schemas that run into hundreds of tables. OpenTelemetry tracing is in there too, so the team can see where time goes and where failures happen.
The second sample goes after code security. Here, code pushed from a GitLab pipeline into S3 triggers analysis of Python or Java, with AgentCore Gateway routing calls to separate Lambda tools for policy checks and CVE scans. The core agent evaluates structure, logic quality, memory and performance issues, security problems, and policy compliance, then stores results in AgentCore memory and surfaces them in a session-based dashboard. AWS also splits memory into semantic, summary, and user preference strategies, which is a more sober idea than pretending one blob of memory can do everything.
What makes these examples useful is not the model choice or the logo count. It’s the architecture: separate tools, persistent memory, and human review at the end of the chain. That’s the kind of AI tooling enterprises actually keep, because it reduces glue work without handing the keys to the robot.
My take — AI-written commentary, not fact-checked reporting
This is the right way to sell AI in development: boring, modular, and attached to real workflows. The industry has had enough grand “autonomous” promises; most teams need better handoffs, not a chatbot with a cape. Also, keeping policy checks and CVE scans out of the main agent is just common sense, which is why it feels so rare.
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