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AWS Transform is AWS’s umbrella service for infrastructure, application, and code transformation. It combines managed migration workflows—such as discovery, assessment, wave planning, landing-zone setup, networking, server rehosting, and containerization—with an Agent Builder Toolkit for creating organization-specific agents. The workflows are agentic and collaborative, not a promise of unattended migration: results depend on inventory quality, assumptions, constraints, approvals, and the AWS capabilities available in your region.
What is AWS Transform?
AWS describes AWS Transform as “a service that helps you accelerate and simplify the transformation of infrastructure, applications, and code with an agentic AI experience.” In practical terms, it is a managed workbench for planning and carrying out several kinds of cloud transformation rather than a single migration command.
The service’s documented scope includes mainframe modernization; migration of virtual and bare-metal servers, including VMware and Hyper-V environments; infrastructure discovery and dependency analysis; migration-wave planning; landing-zone creation; network migration; server rehosting; and source-code containerization for Amazon ECS or Amazon EKS.
Its agentic design matters because specialized agents can work with shared workspaces and hand information between stages. A discovery result can inform dependency analysis, a wave plan can feed landing-zone and network work, and an execution workflow can use the approved plan. However, “agentic” does not mean that AWS Transform independently decides what your business should migrate. Your inventory, application owners, security rules, licensing constraints, recovery objectives, and approval gates remain part of the process.
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What does AWS Transform automate?
AWS Transform can coordinate repeatable migration work, surface dependencies, generate planning options, and assist with implementation. The exact boundary between recommendation and execution varies by workflow and by the permissions you grant.
Discovery and dependency analysis
Discovery collects information about servers, applications, networks, and relationships between workloads. Dependency analysis helps identify which systems communicate and therefore should be considered together. This information is used to avoid creating migration waves that split a tightly coupled application across incompatible schedules.
Assessment and migration-wave planning
Assessment turns inventory data into candidate target architectures, cost scenarios, and sequencing options. Wave planning groups workloads for a move, allowing teams to define pilots, rollback points, business-owner approvals, and production cutovers instead of treating a data center as one undifferentiated batch.
Landing zones and networks
The service can assist with creating a landing zone—the account, identity, security, logging, and governance foundation into which workloads move. It also lists network migration as a supported workflow. Those tasks still require architecture decisions such as account boundaries, IP ranges, connectivity, inspection, DNS, and compliance controls.
Rehosting and containerization
Rehosting moves a server workload with limited application change. Containerization changes the packaging and operating model, and AWS Transform documents source-code containerization for Amazon ECS or Amazon EKS. Containerization is therefore a modernization path, not simply another name for copying a virtual machine.
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Mainframe and application modernization
Mainframe modernization and broader application transformation are included in AWS Transform’s stated scope. The appropriate approach depends on code languages, data stores, transaction behavior, test coverage, and the organization’s tolerance for functional change. A generated plan is an input to engineering review, not proof that a legacy system is ready for production conversion.
How AWS Transform assessments use your data
AWS announced expanded assessment capabilities on May 22, 2026. Assessments can accept several inventory sources, including RVTools exports, configuration-management-database (CMDB) data, AWS Transform discovery output, and third-party discovery exports. This allows a team to start with information it already has and enrich it with additional discovery rather than requiring one collection method for every environment.
Scenario comparisons and what-if assumptions
You can change assumptions and compare scenarios instead of accepting one fixed estimate. Examples include changing target services, utilization assumptions, licensing treatment, growth expectations, or migration timing. The useful result is not a single “AI answer”; it is a set of explicit options showing how a different assumption changes the plan.
Every estimate remains conditional. If the inventory omits a server, understates utilization, misidentifies a dependency, or uses an unrealistic growth rate, the resulting cost and schedule model will be wrong even if the arithmetic is correct. Record the source and date of each inventory set, identify unknowns, and have workload owners validate the assumptions before using an assessment for funding or commitments.
Cost and Cloud Value Framework modeling
The May 2026 announcement describes total-cost modeling that can include Amazon EC2, Amazon FSx, Amazon S3, SQL Server on EC2, and virtual desktops, along with other Cloud Value Framework pillars. These categories can help compare a current environment with multiple AWS designs. They do not automatically include every cost driver: data transfer, support, third-party licenses, staffing, downtime, refactoring, and operational tooling must be checked against the scope of the model you are using.
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AWS Transform versus the Agent Builder Toolkit
The phrase “AWS toolkit for cloud migration” can describe two related layers. Keeping them separate prevents a common misunderstanding.
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| Layer | What it provides | Typical users | What it does not establish |
|---|---|---|---|
| AWS Transform managed service | Specialized agents and guided workflows for discovery, assessment, planning, landing zones, networks, rehosting, containerization, and other transformation tasks. | Organizations planning or executing AWS migrations and modernization. | That every step is autonomous or that every workload can be migrated without engineering review. |
| Agent Builder Toolkit | Tools for creating custom transformation agents and connecting them to an organization’s runbooks, standards, and workflow systems. | Professional services organizations, independent software vendors, and enterprise teams. | A universal migration product, a guaranteed outcome, or a substitute for AWS Transform’s managed workflows. |
AWS’s launch material describes access through the web console, command-line interface, integrated development environments, a Visual Studio extension, agent skills, and Model Context Protocol (MCP) integration. These are ways to use or extend the experience; they are not separate migration engines with identical feature coverage.
Why organizations build custom agents
- Professional services organizations can encode their delivery methods, assessment checklists, and customer-specific runbooks.
- ISVs can embed transformation assistance in products that already manage application or infrastructure workflows.
- Enterprises can express internal security controls, naming standards, approval policies, and architecture patterns in reusable agent behavior.
A custom agent should be treated like production automation: define its inputs, allowed actions, identity, logging, test cases, escalation rules, and rollback behavior. A prompt or skill that produces a plausible plan is not the same as a validated change-management control.
What changed with AWS Transform MGN?
On June 8, 2026, AWS announced that AWS Application Migration Service is now named AWS Transform MGN. The rename places the established managed migration capability under the AWS Transform umbrella.
AWS describes two ways to approach rehosting:
- Direct MGN control: use the MGN console experience to control replication and cutover directly.
- Agentic workflow: use a broader sequence that can include discovery, wave planning, landing-zone setup, network creation, and rehosting or containerization.
Direct control is useful when an experienced team needs precise operational handling of replication and cutover. The agentic path is useful when the organization wants planning and execution steps connected in one guided process. The choice is not permanent; governance may require direct control for sensitive workloads while allowing agent assistance for lower-risk discovery and planning.
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A practical AWS Transform migration sequence
The following sequence mirrors the capabilities AWS documents while keeping human decisions explicit.
- Define scope and owners. Identify applications, environments, business owners, recovery objectives, data classifications, licensing constraints, and the success criteria for the first wave.
- Assemble inventory. Import available RVTools, CMDB, AWS Transform discovery, or third-party discovery exports. Mark the collection date, coverage gaps, stale records, and systems that still need validation.
- Run dependency analysis. Review communication relationships and ownership with application teams. Resolve contradictory records before grouping workloads into waves.
- Build assessment scenarios. Compare candidate targets and enter explicit what-if assumptions. Include utilization, growth, licensing, storage, database, desktop, and data-transfer considerations that affect the estimate.
- Approve a migration wave. Select a pilot, define entry and exit criteria, assign approvers, document rollback, and set a cutover window. Do not treat an automatically suggested grouping as an approval.
- Design or validate the landing zone. Confirm accounts, identity, logging, security controls, connectivity, DNS, IP addressing, and policy guardrails before placing production workloads.
- Choose the execution path. Use direct AWS Transform MGN controls when operational precision is the priority, or use the agentic workflow when connected planning and execution provide more value.
- Test and cut over. Validate replication, application behavior, performance, integrations, backup, monitoring, and rollback in a rehearsal. Obtain business-owner sign-off before production cutover.
- Close the loop. Compare actual utilization, cost, incidents, and recovery behavior with the assessment assumptions. Feed corrected data into the next wave rather than copying an inaccurate baseline.
Human review, security, and governance
Agentic migration is safest when every consequential action has a defined review boundary. Separate read-only discovery permissions from change permissions, use least-privilege roles, and log agent recommendations and executed actions. Require a named owner for network changes, identity changes, data movement, production cutover, and rollback approval.
Keep assumptions versioned. An assessment made from a six-month-old CMDB export should not be presented as a current infrastructure baseline. Store the inventory snapshot, scenario settings, approvals, and exceptions with the migration record so that a cost or architecture dispute can be traced to the underlying input.
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For custom agents, test adversarial and incomplete inputs: missing dependencies, conflicting naming standards, unsupported operating systems, unavailable APIs, and a request that exceeds the agent’s permissions. The desired failure mode is a clear escalation, not a confident but unsafe change.
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What AWS has reported—and what those numbers mean
AWS’s Migration Launch Guide reports that, in the first year, AWS Transform migrated tens of thousands of virtual machines, processed more than 4.5 billion lines of code, and saved 1.69 million hours of manual effort. These are AWS-reported figures, not independently verified measurements, and they describe aggregate experience rather than a guaranteed result for an individual project.
The same launch guide describes modernization as “up to 5x faster” and cites customer-reported operating-cost reductions of “up to 70%.” “Up to” figures identify an upper bound observed in the cited context; they should not be used as a forecast without matching the workload type, baseline, scope, and measurement method.
Pricing, regions, and procurement checks
Current pricing for AWS Transform capabilities was not established in the available official material. Costs may involve the service itself and the AWS resources, storage, data transfer, databases, security services, and support used by the resulting architecture. Obtain a current estimate from AWS pricing documentation and your account team for the exact capabilities and region you plan to use.
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No specific hardware is required by the product description. AWS Transform is a cloud service accessed through documented interfaces and development environments; your project still needs suitable source-environment access, credentials, network connectivity, and people who can validate the outputs.
When AWS Transform is a good fit
- You have a mixed estate and need one place to combine discovery, assessment, wave planning, and execution workflows.
- Your team wants scenario-based cost modeling rather than a single static migration estimate.
- You need to encode internal standards or a professional-services method into reusable agents.
- You want a choice between direct MGN operations and a broader guided, agentic workflow.
When to slow down or use a narrower path
- Inventory coverage is poor or application dependencies are unknown.
- Regulatory, licensing, or data-residency requirements have not been mapped to the target design.
- A workload requires substantial refactoring, but the organization has not funded testing and operational ownership.
- The team expects fully autonomous production migration and has no approval or rollback process.
Bottom line
AWS Transform is best understood as an agent-assisted transformation workbench, not a push-button migration robot. Its managed workflows cover the journey from discovery and assessment through landing zones, networks, rehosting, and containerization; AWS Transform MGN provides the renamed Application Migration Service with both direct and agentic operating paths. The Agent Builder Toolkit extends that foundation so services firms, ISVs, and enterprises can build agents around their own runbooks. The quality of the outcome still depends on accurate inventory, explicit assumptions, regional and pricing checks, and disciplined human approval.
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