AWS agentic AI for VMware migration

AWS Cloud Migration • Agentic AI

Accelerating VMware-to-AWS
discovery, planning, network translation, and multi-tier modernization through autonomous AI
orchestration.

1–2 Days AWS Transform Assessment Bedrock MCP Multi-Agent Automation

Overview

Organizations are increasingly moving VMware-based workloads to the cloud to improve scalability, operational
efficiency, and long-term IT flexibility. However, migrating complex VMware environments can involve extensive
assessment, dependency mapping, network conversion, infrastructure planning, testing, and workload cutover.

AWS is using agentic AI to simplify these migration activities. AI-powered services can analyze VMware
environments, recommend AWS infrastructure configurations, create migration plans, and coordinate complex
workflows. This can help organizations accelerate their cloud journey while improving consistency and reducing
migration risk.

In this blog, we explore two AWS approaches for VMware workload migration: AWS Transform for
VMware and
Amazon Bedrock multi-agent collaboration. While VMware is the primary example, these
capabilities
can also support broader migration and modernization scenarios.

What Is Agentic AI for Cloud Migration?

Traditional cloud migration often requires teams to manually collect infrastructure information, analyze
application dependencies, develop target architectures, create migration waves, and coordinate multiple
migration
activities.

Agentic AI introduces specialized AI agents that can perform or coordinate many of these tasks. Depending on the
solution, agents can analyze infrastructure data, recommend configurations, interact with knowledge bases,
generate infrastructure plans, and orchestrate migration workflows.

For VMware environments, AWS provides two particularly useful approaches:

• ✓AWS Transform for VMware – designed specifically to simplify
  VMware-to-AWS assessment, planning, and migration.
• ✓Amazon Bedrock multi-agent collaboration – provides a flexible
  framework for building specialized AI agents for complex migration and modernization
  scenarios.

Key AWS Agentic AI Concepts

Before comparing the two approaches, it is useful to understand several Amazon Bedrock concepts:

• ✓Bedrock Knowledge Bases: Knowledge Bases provide agents with
  relevant information and documentation for domain-specific tasks. Migration templates, technical
  guidelines, architectural standards, and best practices can be used to provide specialized knowledge
  to agents.
• ✓Bedrock Action Groups: Action Groups allow agents to perform
  specific operations within their assigned domain. They can connect an agent’s reasoning
  capabilities with actions required during migration workflows.
• ✓Bedrock Supervisor Agent: A Supervisor Agent acts as the primary
  orchestrator. It analyzes requests, coordinates specialized agents, and manages communication
  between them to complete complex tasks.
• ✓Bedrock Collaborative Agents: Collaborative Agents are specialized
  sub-agents designed to focus on specific areas of a migration. They work under the coordination of a
  supervisor agent to address complex, multi-step requirements.
• ✓Model Context Protocol (MCP) Servers: MCP servers provide a
  standardized way for large language models to securely interact with external tools and data
  sources. This can help connect AI agents with operational systems and migration tooling.

Key Takeaway: Amazon Bedrock provides the foundational orchestration fabric—uniting
Knowledge Bases, Action Groups, Supervisor Agents, and MCP interfaces—to transform static migration
spreadsheets into intelligent, autonomous migration pipelines.

Option 1: AWS Transform for VMware

AWS Transform for VMware is designed specifically for organizations assessing and migrating VMware workloads to
AWS.

The service can use information from sources such as RVTools, AWS Migration Portfolio
Assessment (MPA), and Migration Evaluator exports to analyze an
organization’s VMware environment. It can generate assessments covering areas such as server configuration
and Windows licensing.

How AWS Transform Supports VMware Migration

The migration process can include:

• ✓Collect VMware inventory data: Seamlessly ingests configuration
  logs and VM resource profiles.
• ✓Assess the existing environment: Evaluates compute, memory,
  storage utilization, and OS compatibility.
• ✓Analyze infrastructure and licensing: Identifies Windows Server
  and SQL licensing optimization opportunities.
• ✓Generate migration recommendations: Recommends right-sized AWS
  target instance families and storage types.
• ✓Create migration waves: Groups servers into logical,
  non-disruptive migration waves based on dependencies.
• ✓Convert VMware networking configurations: Translates port groups,
  VLANs, and subnets into AWS VPC constructs.
• ✓Generate AWS infrastructure recommendations: Delivers complete
  target architecture blueprints.
• ✓Migrate workloads using AWS Application Migration Service (MGN):
  Executes block-level replication and cutovers.

AWS Transform can translate VMware networking configurations into AWS constructs such as VPCs, subnets, and
security groups, while also providing EC2 sizing recommendations and migration plans. Migration plans can be
reviewed and edited before migration activities are initiated.

The platform also provides dashboards for monitoring migration jobs and maintaining work logs, helping teams
maintain visibility throughout the migration process.

Why Organizations May Choose AWS Transform

AWS Transform can be particularly valuable for organizations looking for:

• ✓Faster VMware assessment: Reduces discovery and assessment time to
  approximately 1–2 days.
• ✓Automated migration planning: Eliminates error-prone manual
  spreadsheets.
• ✓Standardized VMware-to-AWS migration: Establishes repeatable
  patterns across large estates.
• ✓Infrastructure sizing recommendations: Optimizes EC2 compute
  instances and EBS storage allocations.
• ✓Network configuration conversion: Converts VMware vSwitches and
  security policies into AWS VPC constructs.
• ✓Windows licensing analysis: Optimizes Bring-Your-Own-License
  (BYOL) vs. license-included configurations.
• ✓Simplified wave planning: Organizes execution timelines and
  minimizes downtime.
• ✓Built-in cost optimization: Provides comparative TCO estimations
  and cost-effective instance targets.
• ✓Interactive explanations of recommendations: Transparent AI
  rationales for sizing and architecture decisions.

Rapid Time-to-Value: According to AWS data, AWS Transform assessments take approximately
1–2 days compared with 2–3 weeks for traditional manual approaches, making
it exceptionally attractive for organizations seeking rapid time-to-value.

Option 2: Amazon Bedrock Multi-Agent Collaboration

While AWS Transform is purpose-built for VMware migrations, Amazon Bedrock multi-agent collaboration provides a
more flexible architecture for organizations with complex migration and modernization requirements.

In this model, a Supervisor Agent coordinates multiple specialized agents. For example, an
organization could create separate agents for:

• ✓Portfolio assessment agent: Ingests inventory datasets, analyzes
  multi-app interdependencies, and plans sprint waves.
• ✓Infrastructure design agent: Creates Architecture-as-Code
  blueprints, Terraform templates, and cost models.
• ✓Migration orchestration agent: Manages tool integration,
  replication status, and automated cutover triggers.
• ✓Operations agent: Oversees post-migration validation, health
  checks, monitoring integration, and governance audits.

Each specialized agent can have its own Action Groups and knowledge resources.

Example Multi-Agent Migration Architecture

A portfolio agent could manage activities such as migration wave planning, sprint planning, and portfolio
analysis. An infrastructure agent could handle architecture documentation, Infrastructure-as-Code templates, and
cost estimation. Migration orchestration and operations agents can then focus on coordinating and managing
subsequent migration activities.

Migration information such as RVTools exports, business decisions, and migration documentation can be stored in
Amazon S3. AWS Lambda can help synchronize this information with
Amazon Bedrock Knowledge Bases, providing agents with centralized migration context.

MCP Integration

MCP servers can further extend this architecture by providing standardized connections between AI agents and
external tools or operational systems.

This makes Amazon Bedrock multi-agent collaboration particularly interesting for organizations that need to
build custom migration workflows, reusable automation patterns, and sophisticated transformation processes.

Architectural Decision Guide

Which AWS Agentic AI Migration Approach Should You Choose?

Follow the decision paths below to match your workload portfolio with the optimal AI-driven
migration pattern.

Step 1: Evaluate Workload Complexity, Customization Needs & Modernization Scope

1
Standard VMware Rehost Primarily lift-and-shift with standard OS sizing and fast network conversion? AWS
TRANSFORM

AWS
Transform for VMware Automated 1–2 day assessment,
VPC network translation, wave planning & MGN execution.

2
Complex Modernization Needs custom IaC, complex dependencies, MCP tool integration, or refactoring? BEDROCK
MULTI-AGENT

Bedrock
Multi-Agent Collaboration Specialized collaborative
agents, custom IaC templates, MCP tool integration & refactoring.

3
Enterprise Portfolio Diverse estate with standard VMs alongside mission-critical legacy applications? TWO-PATTERN
STRATEGY

Adopt Both
Approaches Assess & rehost with AWS
Transform; modernize & customize with Amazon Bedrock agents.

AWS Transform vs. Amazon Bedrock Multi-Agent Collaboration

The two approaches address different migration requirements.

AWS Transform’s strengths include automated assessments, faster network configuration conversion, cost
optimization, wave planning, integrated explanations, and simplified operations. Amazon Bedrock’s
strengths include flexible agent specialization, advanced reasoning, supervisor/routing modes, tracing and
debugging, knowledge-base integration, and MCP integration.

When Should You Choose AWS Transform for VMware?

AWS Transform is well suited for organizations undertaking relatively straightforward VMware-to-AWS rehost
migrations.

It can be a strong option when an organization:

• ✓Has a large VMware estate to assess quickly
• ✓Wants standardized migration planning
• ✓Needs automated infrastructure sizing
• ✓Has limited cloud migration expertise
• ✓Wants faster assessment and planning
• ✓Is primarily pursuing rehost migration patterns
• ✓Has standard networking and licensing requirements

For organizations focused on quickly moving VMware workloads to AWS with a standardized approach, AWS Transform
can provide a simpler starting point.

When Should You Choose Amazon Bedrock Multi-Agent Collaboration?

Amazon Bedrock multi-agent collaboration becomes more relevant when migration requirements go beyond
standardized rehosting.

It can be appropriate for organizations dealing with:

• ✓Complex application dependencies
• ✓Custom transformation patterns
• ✓Replatforming requirements
• ✓Refactoring initiatives
• ✓Multi-phase modernization
• ✓Custom migration workflows
• ✓Advanced dependency analysis
• ✓Reusable migration automation
• ✓Non-VMware workloads

Organizations with mature cloud engineering teams may also benefit from the flexibility of designing specialized
agents and connecting them with existing tools and operational systems.

Can Organizations Use Both Approaches?

Yes. For organizations with diverse workload portfolios, using both approaches can provide broader migration
coverage.

A practical strategy is to use AWS Transform for VMware for the initial assessment and standardized rehost
migration of VMware workloads. More complex applications can then be handled using Amazon Bedrock multi-agent
collaboration, particularly where modernization, replatforming, refactoring, or custom automation is required.

The Two-Pattern Strategy: Assess and rehost with AWS Transform → Modernize and
customize with Amazon Bedrock agents.

Such an approach can help organizations apply the right level of automation to each workload rather than forcing
every application into the same migration pattern.

Benefits of Agentic AI for VMware Migration

Agentic AI can bring several advantages to cloud migration programs:

• ✓Faster Assessment: Automated analysis can reduce the time required
  to understand large VMware environments and identify migration opportunities.
• ✓Improved Planning: AI-powered recommendations can help teams
  develop infrastructure configurations, migration waves, and transformation plans based on available
  environment data.
• ✓Reduced Manual Effort: Automation can reduce repetitive activities
  involved in infrastructure analysis, planning, configuration, and migration coordination.
• ✓Better Scalability: Organizations can apply standardized migration
  processes across larger workload portfolios while using specialized agents for more complex
  requirements.
• ✓Greater Visibility: Interactive explanations, dashboards, tracing,
  and work logs can help teams understand recommendations and monitor migration activities.
• ✓Flexible Modernization: With Amazon Bedrock multi-agent
  collaboration, organizations can build customized workflows for migration, replatforming,
  refactoring, and other transformation scenarios.

Considerations Before Adopting Agentic AI for Migration

Agentic AI does not eliminate the need for migration strategy, architecture governance, testing, and human
oversight. Organizations should evaluate:

• ✓VMware environment complexity and inventory volume
• ✓Application dependencies and database coupling
• ✓Network architecture and hybrid connectivity constraints
• ✓Security requirements and compliance policies
• ✓Licensing considerations (Windows, SQL, and enterprise software)
• ✓Migration and modernization business objectives
• ✓Internal AWS expertise and operational capabilities
• ✓Required workflow customization and operational responsibilities
• ✓Cost of AI agents, foundational models, and knowledge resources

AWS Transform has a more focused VMware scope, while Amazon Bedrock multi-agent collaboration introduces greater
flexibility but also requires more architectural planning and operational management.

Results & Benefits

By harnessing AWS agentic AI for VMware workload migrations, enterprise IT leaders can achieve significant
acceleration and precision throughout their cloud transformation:

• ✓Compressed Discovery Timelines: Automated parsing of RVTools and
  MPA data accelerates environment assessments from weeks to just 1–2 days.
• ✓Flawless Network Conversion: Automated translation of VMware port
  groups and security policies directly into AWS VPCs, subnets, and Security Groups.
• ✓Right-Sized Cloud Economics: AI-driven rightsizing recommendations
  and license analysis minimize overprovisioning and ongoing AWS operational costs.
• ✓Intelligent Multi-Agent Workflows: Bedrock Supervisor and
  Collaborative agents automate IaC generation, dependency tracking, and multi-tier cutovers.
• ✓Mitigated Cutover Risk: Non-disruptive wave planning and
  Application Migration Service (MGN) integration ensure zero unexpected downtime.

How Princeton IT Services Can Help

Migrating VMware workloads to AWS requires more than simply moving virtual machines. Organizations need a
structured approach to assessment, dependency analysis, architecture planning, security, networking, migration
execution, and post-migration optimization.

Princeton IT Services can help organizations evaluate their VMware environment and determine the right AWS
migration strategy based on workload complexity, business objectives, and modernization requirements.

Our approach can help organizations identify where standardized automation can accelerate migration and where
customized AI-driven workflows may provide greater value.

Whether your organization is planning a VMware-to-AWS rehost migration or a broader cloud modernization
initiative, selecting the appropriate migration pattern is essential for balancing speed, cost, flexibility, and
risk.

Conclusion

Agentic AI is changing how organizations approach cloud migration. AWS Transform for VMware provides a focused
solution for assessing and planning standardized VMware-to-AWS migrations, while Amazon Bedrock multi-agent
collaboration provides a flexible framework for complex migration and modernization scenarios.

For many enterprises, the best strategy may not be choosing one solution exclusively. AWS Transform can
accelerate standardized VMware assessments and rehost migrations, while Bedrock multi-agent architectures can
address applications requiring specialized reasoning, custom workflows, or modernization.

By matching the right AI-powered migration approach to each workload, organizations can build a more efficient
and scalable path from VMware environments to AWS while reducing manual effort and migration risk.

Princeton IT Services can help your organization evaluate its VMware workloads, define an AWS migration
strategy, and identify opportunities to use AI-driven automation across the migration lifecycle.