How to Maximize AI Cost Visibility and Agent Management on Amazon Bedrock

Introduction

As organizations accelerate their AI initiatives, two major challenges emerge: understanding who’s spending what on foundation models, and preventing a chaotic sprawl of AI agents. Amazon Bedrock has introduced powerful solutions this week to tackle both. With cost allocation by IAM user and role, you can now track model inference expenses down to the team or cost center. The Claude Mythos preview brings state-of-the-art cybersecurity capabilities for vulnerability detection, while the AWS Agent Registry lets you catalog and govern agents centrally. This guide walks you through each feature step by step.

What You Need

• An active AWS account with appropriate permissions (IAM, Billing, Bedrock).
• Access to Amazon Bedrock in a supported Region.
• For Claude Mythos: eligibility for the gated preview (internet-critical organizations or open source maintainers).
• Basic familiarity with the AWS Management Console, CLI, or SDK.

Step 1: Enable Cost Allocation by IAM Principal in Amazon Bedrock

Gain granular visibility into model spending by tagging IAM users and roles with attributes like team or cost center.

1. Tag your IAM principals – In the IAM console, assign tags to users or roles. For example, add a tag CostCenter: Engineering or Team: AI-Research.
2. Activate the tags in Billing – Go to the Billing and Cost Management console, navigate to Cost Allocation Tags, and activate the IAM principal tags you created.
3. View cost data – After activation, open AWS Cost Explorer or the detailed Cost and Usage Report. Filter resources by your active tags to see model inference costs per IAM principal.
4. Use alongside Bedrock agents – When you run tools like Claude Code or deploy agents across teams, these tags follow the requests, making it easy to track spending by department.

For full setup details, see the IAM principal cost allocation documentation.

Step 2: Access and Use Claude Mythos (Preview) for Cybersecurity

Claude Mythos, Anthropic’s most advanced model for security, is available as a research preview through Project Glasswing.

1. Request access – Visit the Project Glasswing page and apply for the allowlist. Priority is given to internet-critical companies and open source maintainers.
2. Enable the model in Bedrock – Once approved, go to the Amazon Bedrock console, select Model access, and request access for the Claude Mythos model.
3. Run security analyses – Use the Bedrock API, SDK, or Playground to submit codebases or software packages for vulnerability scanning. Example prompt: “Analyze this repository for potential zero-day exploits in critical network services.”
4. Integrate into CI/CD pipelines – Automate Mythos scans by adding a call to the Bedrock InvokeModel API in your build process. Analyze each commit for new security issues before deployment.

This model excels at complex reasoning and coding tasks, making it ideal for proactive threat discovery.

Step 3: Set Up AWS Agent Registry for Centralized Governance

Avoid duplication and maintain control by creating a private catalog of all agents, tools, and MCP servers.

1. Enable AgentCore – Navigate to the AgentCore Console and activate the Agent Registry feature. You’ll also need to set up AWS CloudTrail for audit logging.
2. Create a catalog – Define a schema for your agents (name, description, capabilities, owner). Start adding existing agents and tools by providing metadata and IAM roles.
3. Register MCP servers and custom resources – For any MCP server you use, register it in the registry so teams can discover it. Attach documentation and version information.
4. Enable approval workflows – Set up approval rules to require sign-off before a new agent is published. This ensures quality and security standards.
5. Search and reuse – Developers can now query the registry using semantic search or keywords from their IDE (via MCP server integration). Instead of building a new agent, they find an existing one that meets their needs.
6. Monitor with CloudTrail – All registry actions (add, update, approve) are recorded, providing full auditability for compliance.

Bonus: Making S3 Accessible as a File System

While not the core focus, Amazon S3 Files was announced and can complement your agent workflows. It mounts S3 buckets as file systems using EFS technology, giving compute resources low-latency access to S3 data. Use it to feed large datasets to your agents or store agent outputs seamlessly. Follow the standard S3 bucket setup and then enable the S3 Files mount point.

Tips for Best Results

• Start small with cost tags – Test tagging on a single IAM role before rolling out across all users. Validate that data appears in Cost Explorer within 24 hours.
• Combine tags with budgets – Once you see per-team costs, create AWS Budgets alerts to notify teams when their AI spending exceeds thresholds.
• Wrap Mythos scans in security review cycles – Use Claude Mythos as a complementary tool alongside static analysis and manual reviews, not a replacement.
• Govern agents early – Before agent proliferation grows, establish a naming convention and metadata standard in the Agent Registry. This makes discovery far easier later.
• Monitor for unused agents – Regularly review the registry for agents that haven’t been used in 90 days and consider deprecating them to reduce attack surface.
• Leverage MCP from IDEs – Encourage developers to install the MCP client in VS Code or JetBrains to search the registry without leaving their editor.

With these three features – cost allocation, Claude Mythos, and Agent Registry – you can bring order, security, and accountability to your AI development lifecycle on AWS.