AWS uses agentic AI to streamline and automate many of the manual steps in VMware-to-AWS migrations.

Instead of relying on spreadsheets and one-off scripts, AI-powered tools can:

• Assess on-premises VMware servers and map complex application dependencies.
• Recommend optimized resource allocations and EC2 sizing based on your current x86 footprint and Windows licensing.
• Generate precise migration plans that cover networking, security, and cutover workflows.

During the mobilize phase, agents translate your on-premises architecture into AWS-native constructs such as VPCs, subnets, security groups, and network connectivity patterns. During execution, they can orchestrate replication, testing, and coordinated cutovers using AWS Application Migration Service (MGN).

This approach can significantly compress planning timelines. For example, translating VMware networking configurations to AWS constructs (VPCs, subnets, transit gateways, internet gateways) can typically be done in about one hour, which is described as nearly 80x faster than the roughly two work weeks often required with manual methods.

The result is a migration process that is more predictable, easier to explain to stakeholders, and better aligned with operational excellence and risk reduction goals.

Both options use agentic AI, but they target different levels of migration complexity.

Use AWS Transform for VMware when:

• You are doing straightforward VMware-to-AWS rehost migrations.
• You want a rapid assessment and standardized plan. The assessment typically takes 1–2 days instead of the 2–3 weeks common with manual approaches.
• You need automated infrastructure sizing and translation of VMware networking into AWS constructs (VPCs, subnets, security groups, transit gateways, internet gateways).
• Your team has limited cloud expertise and needs a guided, low-friction path.

With AWS Transform, you import inventory data (for example from RVTools, Migration Portfolio Assessment (MPA), or Migration Evaluator) and, if applicable, NSX exports. The service then:

• Analyzes x86 servers and Windows licensing.
• Generates transformation and migration plans.
• Provides an AI-driven web interface where you can chat with agents, review, and refine plans.
• Lets you trigger server migration via AWS Application Migration Service (MGN).

Use Amazon Bedrock multi-agent collaboration when:

• You face complex migration or modernization scenarios (replatform, refactor, or multi-phase transformations).
• You have intricate application dependencies or custom transformation patterns.
• Your organization has a mature cloud team and wants reusable migration patterns.

In this model, you create a supervisor agent and several specialized sub-agents (for portfolio assessment, infrastructure, migration orchestration, operations). Each sub-agent uses:

• Knowledge Bases (for templates, best practices, documentation).
• Action Groups (for tasks like wave planning, sprint planning, IaC generation, cost estimates).
• MCP servers to securely connect to external tools and data sources.

Many enterprises choose a combined approach:

• Start with AWS Transform for VMware for initial assessment and rehost patterns.
• Use Amazon Bedrock multi-agent collaboration for more complex modernization and non-VMware workloads.

To get value from AWS’s agentic AI for VMware migrations, you mainly need a reliable inventory of your current environment and a clear separation of responsibilities between tools.

For AWS Transform for VMware:

1. Collect VMware inventory data
   Use collectors or exports to capture your VMware footprint, for example:
   • RVTools exports
   • AWS Migration Portfolio Assessment (MPA) files
   • Migration Evaluator exports
   If you use VMware NSX, follow the NSX Import/Export steps to capture network configuration.
2. Upload data into AWS Transform
   The service analyzes x86 servers and Windows licensing, then builds an assessment and transformation plan.
3. Use the AI-driven web interface
   Interact with autonomous agents via chat to:
   • Understand how recommendations were made.
   • Review and adjust EC2 sizing, networking mappings, and migration waves.
4. Execute migration
   Once plans are approved, you can initiate server migration using AWS Application Migration Service (MGN) and monitor progress via dashboards and work logs.

For Amazon Bedrock multi-agent collaboration:

1. Organize migration knowledge and artifacts
   Store items such as RVTools exports, business decisions, and migration documentation in Amazon S3.
2. Sync content into Knowledge Bases
   Use an AWS Lambda function to keep Amazon Bedrock Knowledge Bases updated with your latest migration data and templates.
3. Define specialized agents
   Through the agent builder workflow, create sub-agents for areas like portfolio assessment, infrastructure, orchestration, and operations. Attach:
   • Relevant Knowledge Bases (best practices, templates, guidelines).
   • Action Groups (wave planning, sprint planning, IaC generation, cost estimation, etc.).
4. Configure the supervisor agent
   Enable multi-agent collaboration, assign each sub-agent a collaborator name, and define its role in the migration process.
5. Integrate with tools via MCP
   Optionally, connect agents to external tools and data sources using Model Context Protocol (MCP) servers for standardized, secure access.

Both approaches are designed to help you reimagine migration planning and execution by combining your existing VMware data with AI-driven guidance, while still giving your team control over final decisions and changes.