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sample-bedrock-readiness-agent

Free

Sample AI agent that assesses an AWS account's Amazon Bedrock production readiness across six dimensions - IAM governance, data retention, quota, model selection, cost, and observability - using read-only APIs, and ge...

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Overview

The Bedrock Readiness Agent is a standalone assessment agent built with the Strands Agents SDK that evaluates an AWS account's readiness to run Amazon Bedrock at production scale. It assesses the Bedrock environment across six dimensions: IAM governance, data retention, quota headroom, model selection fitness, cost projection, and operational observability. The agent runs on Amazon Bedrock AgentCore or locally with AWS credentials, and it is read-only, querying configuration and metrics without modifying resources. It generates severity-rated findings with remediation templates in CloudFormation and Terraform that can be applied directly. The agent uses a reasoning model (Claude Sonnet) and a set of six assessment tools to produce an overall readiness verdict, per-dimension findings, remediation templates, a priority action list, and a readiness score breakdown. It is sample code intended for non-production usage, and users should work with their security and legal teams to meet organizational requirements before deployment.

Pricing

Free

Detailed plans have not been confirmed in our catalog. Check the official website for current limits and billing terms.

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Prices and limits may change. Confirm the currency, billing period, seat minimum and usage caps on the official website.

Use Cases

  • Teams use the agent to assess whether an AWS account is ready to run Amazon Bedrock at production scale.
  • Teams use the agent to evaluate IAM governance by scanning roles for wildcard permissions and checking guardrails.
  • Teams use the agent to verify Zero Data Retention (ZDR) configuration for Bedrock.
  • Teams use the agent to check quota headroom by examining throttle history and detecting CRIS usage.
  • Teams use the agent to analyze model diversity, legacy usage, and cost efficiency for model fitness.
  • Teams use the agent to project costs by checking batch eligibility, spend trends, and invocation logging.
  • Teams use the agent to validate observability by checking that alarms exist and all five VPC endpoint types are present.
  • Teams use the agent to generate CloudFormation and Terraform remediation templates for HIGH/CRITICAL findings.

Features

AI assistant

Natural language interface

Workflow automation

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