One of the most common issues in my day-to-day Code Review work is that Code Review still relies heavily on TechLead's experience.

Reviewers must compare themselves:

• Coding Convention
  • Security Checklist
  • API/SQL Standards
  • Architecture Guidelines
  • Best Practices

And as the number of PRs increases, the basic mistakes continue to repeat.

Not to mention that each TechLead may have a different way of evaluating.

One use case I'm testing is combining Amazon Bedrock AgentCore + Knowledge Base + GitLab + Kiro to build an AI Code Review Agent.

The flow is as follows:

Developer → Kiro → Agent → GitLab → Knowledge Base → Review

The agent will:

1. Get PR/Diff from GitLab.
   1. Identify the relevant Engineering Standards.
   2. Retrieve Coding/Security/Architecture Rules from the Knowledge Base.
   3. Analyze code changes based on the rules themselves.
   4. Return the Finding, Severity, Recommendation, and Reference to the respective rule.

Thus, the Knowledge Base is no longer just a place to store documents.

It becomes Engineering Knowledge that is fed directly into the Development Workflow.

AI no longer only supports developers at every step, but begins to become part of the Software Development Lifecycle.

AI #AIAgent #AgenticAI #AmazonBedrock #AgentCore #SoftwareEngineering #CodeReview #DevOps #Kiro #AIFirst