AWS AI Agent Migration Planning: Build a Smarter AWS Migration Roadmap with AWS Transform

Cloud migration is not just about moving virtual machines from an on-premises data center to AWS. The real challenge is understanding application dependencies, choosing the right migration strategy, estimating costs, managing business risks, and deciding which workloads should move first.

Muhammad Umair Ashraf

Cloud Data Engineer

Published Oct 22, 2026

Last modified Oct 22, 2026

Introduction

For a small environment, spreadsheets might be enough. For an enterprise with hundreds or thousands of servers, databases, and interconnected applications, manual planning becomes difficult to maintain. A missing dependency or incorrect migration sequence can cause unexpected downtime, additional costs, and failed cutovers.

This is where AI agent-powered migration planning can make a difference.

AWS Transform provides an agentic AI-powered migration experience that helps teams analyze infrastructure discovery data, group applications, identify dependencies, and build migration wave plans through an interactive workflow. Instead of treating migration planning as a static spreadsheet exercise, teams can refine their plans as new information becomes available.

In this article, we will explore how AI agents can support AWS migration planning, how AWS Transform fits into the process, and how to build a small local prototype that validates infrastructure inventory before an AI-assisted planning exercise.

1. Why Traditional Migration Planning Is Challenging

Before moving workloads to AWS, teams need answers to several important questions:

Which servers and applications are currently running?
Which applications depend on shared databases, queues, or services?
Which workloads are suitable for an initial migration wave?
Should an application be rehosted, replatformed, refactored, or retired?
What are the expected costs, downtime requirements, and operational risks?
Which teams must approve the migration before execution?

These questions require technical discovery and business context. Server configuration alone cannot tell us whether an application can tolerate downtime or whether a database is shared by a business-critical service.

Traditional planning often relies on manual inventory collection, meetings, spreadsheets, and repeated reviews. When infrastructure changes, the migration plan can quickly become outdated.

An AI-assisted workflow can help organize the available information, identify potential relationships, suggest migration groups, and make it easier to update the plan. However, the recommendations still need to be validated against real application behavior and business requirements.

2. What Is AWS Transform?

AWS Transform is an AWS service that uses agentic AI to help accelerate infrastructure migration and technology modernization.

For migration planning, AWS Transform can analyze discovered on-premises infrastructure and guide teams through an interactive workflow to:

Review and scope discovered resources.
Group infrastructure into applications.
Identify applications that must move together.
Organize those groups into migration waves.
Refine the plan as dependencies, priorities, or inventory data change.

The service applies AWS Prescriptive Guidance to help teams develop a migration plan. Its recommendations can be reviewed and adjusted by the migration team before proceeding.

Official documentation: https://docs.aws.amazon.com/transform/latest/userguide/transform-vmware-review-groupings-and-waves.html

The important distinction is that AI helps accelerate analysis and planning; it does not replace architecture review, business approval, testing, or operational readiness checks.

3. How an AI-Assisted AWS Migration Workflow Works

A practical migration workflow can be organized into five stages.

Stage 1: Discover the existing environment

Collect information about the source environment, including:

Server names, operating systems, CPU, and memory
Resource utilization
Application ownership and business criticality
Database and application dependencies
Network communication patterns
Recovery point objective (RPO) and recovery time objective (RTO)
Licensing, compliance, and maintenance constraints

AWS Application Discovery Service helps collect configuration and usage information about on-premises servers and databases. AWS Migration Hub can provide a centralized view of discovered resources and migration status.

Reference: https://docs.aws.amazon.com/application-discovery/latest/userguide/what-is-appdiscovery.html

Stage 2: Analyze and group applications

AI-assisted planning can help identify relationships between servers and applications.

For example, an application server may depend on a database server. Moving the application without considering that dependency could cause an outage.

The team should distinguish between:

Hard dependencies: A system cannot function correctly without the dependency.
Soft dependencies: A relationship exists, but the systems may be able to migrate separately.
Business dependencies: Systems must move together because of operational, ownership, or business requirements.

AWS Transform supports application grouping and move-group planning using technical and business context. The quality of the result depends on the accuracy and completeness of the information provided.

Stage 3: Recommend a migration strategy

Each application needs a suitable migration strategy. A simple lift-and-shift approach is not always the best option.

Common strategies include:

Rehost: Move the application with minimal changes.
Replatform: Make limited changes to use managed AWS services or another suitable platform.
Refactor: Redesign the application to take better advantage of cloud-native capabilities.
Repurchase: Replace the existing application with a different product, such as a SaaS solution.
Retire: Decommission an application that is no longer required.
Retain: Keep an application where it is for now.
Relocate: Move an environment or workload with limited architectural changes, where the chosen platform and migration path support it.

The right strategy depends on cost, technical compatibility, business value, risk, and the time available for migration. An AI recommendation should be treated as a proposal, not an automatic decision.

Stage 4: Build migration waves

A migration wave is a group of applications or move groups scheduled to migrate during a particular period.

A good plan considers dependencies, business criticality, application complexity, team capacity, and cutover requirements.

For example, a low-risk internal application with few dependencies may be a suitable early candidate. A payment application with a shared database and strict recovery requirements may need a separate assessment and a more carefully controlled migration.

AWS Transform supports iterative migration planning, allowing teams to review application groups, generate move groups, and build waves.

Stage 5: Review, approve, and execute

Before migration execution, validate the proposed plan with application owners, cloud architects, database teams, security teams, and operations.

The approved plan should include:

Target architecture and sizing assumptions
Security and networking requirements
Backup and recovery arrangements
Testing and acceptance criteria
Cutover runbook
Rollback plan
Business and technical approvals

Execution can then use appropriate migration services, such as AWS Application Migration Service for supported server migrations or AWS Database Migration Service for supported database migration scenarios.

AWS Transform's planning workflow and the services used to execute a migration have distinct responsibilities. A generated plan does not mean the migration has already been completed.

4. Reference Architecture

A simplified AI-assisted migration architecture looks like this:

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On-Premises Infrastructure
          |
          v
Discovery and Inventory Collection
          |
          v
Inventory + Dependency + Business Context
          |
          v
AWS Transform Migration Planning
          |
          v
Application Groups and Move Groups
          |
          v
AI-Assisted Migration Wave Plan
          |
          v
Human Review and Approval
          |
          v
Controlled Migration Execution
          |
          v
Testing, Monitoring, and Optimization

The workflow separates planning from execution. Discovery data gives the planning process context, AI helps organize and evaluate that context, and human approval ensures the final plan reflects real business and technical requirements.

AWS Migration Hub and other appropriate AWS tools can help teams track migration progress. The exact services and integrations depend on the source environment and the migration approach selected.

5. Hands-On Mini Project: Prepare an Inventory for AI Migration Planning

Let's build a small prototype that validates an infrastructure inventory before it is used in a migration-planning exercise.

This project does not create AWS resources or call AWS APIs. It demonstrates a useful preparation step that can be performed locally before using AWS Transform or another approved planning workflow.

Project structure

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aws-ai-migration-planner/
├── inventory.csv
└── validate_inventory.py

Step 1: Create a sample inventory

Create a file named inventory.csv:

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server_id,application,environment,os,cpu_cores,memory_gib,dependency,business_criticality
srv-001,portal-app,production,Linux,4,16,portal-db,medium
srv-002,portal-db,production,Linux,8,32,none,high
srv-003,reporting-app,production,Windows,4,16,reporting-db,low
srv-004,reporting-db,production,Windows,8,32,none,low

This synthetic inventory represents two applications:

portal-app depends on portal-db.
reporting-app depends on reporting-db.

The business criticality field is illustrative. In a real migration, the application owner must confirm the classification and the dependencies.

Step 2: Create an inventory validation script

Create validate_inventory.py:

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import csv
from pathlib import Path

INVENTORY_FILE = Path("inventory.csv")

REQUIRED_FIELDS = [
    "server_id",
    "application",
    "environment",
    "os",
    "cpu_cores",
    "memory_gib",
    "dependency",
    "business_criticality",
]

VALID_CRITICALITY = {"low", "medium", "high"}

def validate_inventory():
    if not INVENTORY_FILE.exists():
        print(f"ERROR: {INVENTORY_FILE} was not found.")
        return False

    errors = []

    with INVENTORY_FILE.open(
        mode="r", newline="", encoding="utf-8"
    ) as file:
        reader = csv.DictReader(file)

        if not reader.fieldnames:
            print("ERROR: Inventory has no header row.")
            return False

        missing_columns = set(REQUIRED_FIELDS) - set(reader.fieldnames)

        if missing_columns:
            print(
                "ERROR: Missing columns:",
                ", ".join(sorted(missing_columns)),
            )
            return False

        rows = list(reader)

    if not rows:
        errors.append("Inventory contains no server records.")

    seen_ids = set()
    known_ids = set()

    for line_number, row in enumerate(rows, start=2):
        server_id = (row.get("server_id") or "").strip()

        if not server_id:
            errors.append(f"Line {line_number}: server_id is missing.")
        elif server_id in seen_ids:
            errors.append(
                f"Line {line_number}: duplicate server_id {server_id}."
            )
        else:
            seen_ids.add(server_id)
            known_ids.add(server_id)

        for field in REQUIRED_FIELDS:
            if not (row.get(field) or "").strip():
                errors.append(
                    f"Line {line_number}: {field} is missing."
                )

        for field in ("cpu_cores", "memory_gib"):
            value = (row.get(field) or "").strip()

            try:
                number = float(value)
                if number <= 0:
                    errors.append(
                        f"Line {line_number}: {field} must be positive."
                    )
            except ValueError:
                errors.append(
                    f"Line {line_number}: {field} must be numeric."
                )

        criticality = (
            row.get("business_criticality") or ""
        ).strip().lower()

        if criticality and criticality not in VALID_CRITICALITY:
            errors.append(
                f"Line {line_number}: invalid business criticality "
                f"'{criticality}'."
            )

    for line_number, row in enumerate(rows, start=2):
        dependency = (row.get("dependency") or "").strip()

        if dependency and dependency.lower() != "none":
            # This sample expects dependencies to reference an
            # application name, not necessarily a server ID.
            known_applications = {
                (item.get("application") or "").strip()
                for item in rows
            }

            if dependency not in known_applications:
                errors.append(
                    f"Line {line_number}: dependency '{dependency}' "
                    "does not match a known application."
                )

    if errors:
        print("Inventory validation failed:")
        for error in errors:
            print(f"- {error}")
        return False

    print(f"Inventory validation passed: {len(rows)} records.")
    print("Next step: review dependencies and prepare the data")
    print("for an approved migration-planning workflow.")
    return True

if __name__ == "__main__":
    raise SystemExit(0 if validate_inventory() else 1)

Step 3: Run the validator

Make sure Python 3 is installed, then run:

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python validate_inventory.py

Expected output:

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Inventory validation passed: 4 records.
Next step: review dependencies and prepare the data
for an approved migration-planning workflow.

This basic validator catches missing fields, duplicate server IDs, invalid numeric values, and some dependency-reference errors.

For production use, extend it to handle your actual inventory schema, multiple dependency types, shared services, database relationships, ownership records, and data-quality checks. You should also validate dependencies against the correct identifier type: the sample CSV uses application names in its dependency column.

6. Use an AI Agent to Draft a Migration Plan

After the inventory is validated, an architect can use an approved AI-assisted migration-planning workflow to evaluate the data.

For AWS Transform, follow the current user guide and use the supported discovery and migration-planning process. Do not assume that the local CSV from this demonstration is automatically accepted as a direct upload format for every AWS Transform workflow.

Here is a reusable prompt for an AI planning exercise using the synthetic inventory or an approved, sanitized inventory extract:

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Act as an AWS migration planning assistant.

Analyze the supplied infrastructure inventory and propose a
draft migration plan.

Requirements:

1. Identify applications and their dependencies.
2. Identify potential hard, soft, and business dependencies.
3. Suggest a suitable migration strategy for each application.
4. Group applications that may need to migrate together.
5. Propose migration waves based on complexity, business
   criticality, risk, and team readiness.
6. List missing information and assumptions.
7. Identify security, compliance, downtime, and rollback risks.
8. Explain the reasoning behind each recommendation.
9. Do not invent cost estimates when pricing or utilization
   data is missing.
10. Do not execute changes or assume that the plan is approved.

Return a table with:
Application, Proposed Strategy, Dependencies, Suggested Wave,
Risks, Missing Information, and Required Approvals.

Clearly label all recommendations as provisional until
validated by the relevant technical and business owners.

This prompt is a general planning aid, not an AWS Transform API or a guarantee of a particular output. AWS Transform provides its own interactive workflow for migration planning.

7. Example: A Draft Migration Wave Plan

Based on our four-server sample inventory, a planning assistant might suggest the following initial sequence.

Wave	Application	Draft approach	Reasoning
Wave 1	Reporting application and database	Assess for rehost or replatform	Lower stated business criticality; validate dependencies and test readiness first
Wave 2	Portal application and database	Assess for rehost or replatform	Portal is marked medium criticality, while its database is marked high criticality

This is only an illustrative plan. It is not a validated AWS recommendation.

Although the reporting workload appears less critical, the team must still confirm its dependencies, business calendar, security requirements, and recovery procedures. The portal database is marked high criticality, so its owners should confirm downtime limits, consistency requirements, and rollback options before a cutover is scheduled.

A real plan should also consider infrastructure readiness, resource utilization, target architecture, licensing, migration tooling, team availability, and business deadlines.

The goal is not to generate the most attractive table. The goal is to produce a plan that can be safely executed and measured.

8. Security and Human Approval: Keep the Migration Under Control

AI-assisted migration planning can influence decisions about production infrastructure. It therefore needs clear boundaries.

Apply least privilege

Give users and services only the permissions they need. Separate discovery, planning, and execution responsibilities where appropriate. Avoid giving an AI agent unrestricted production access simply because it can generate a plan.

Protect inventory data

Infrastructure inventories may contain internal hostnames, network details, software versions, and other sensitive operational information.

Use approved data-sharing paths.
Remove credentials, tokens, and secrets from inventory files.
Apply appropriate encryption and access controls.
Limit access to planning reports and exported migration data.
Follow your organization's data-handling and retention policies.

Require explicit approval for high-impact actions

The following actions should require review and authorization under your change-management process:

Production cutovers
Network or firewall changes
Database migrations
Resource deletion or retirement
IAM policy changes
Changes affecting availability or recovery objectives

The approval should be tied to a specific version of the plan, with the approver, decision time, and relevant test evidence recorded.

Validate before execution

For every migration wave, verify:

The application and infrastructure inventory is current.
Critical dependencies have been confirmed with owners.
Target infrastructure and security controls are ready.
Backups and recovery procedures have been tested.
Acceptance criteria and rollback conditions are documented.
Monitoring and operational ownership are in place.

AI-generated plans should remain proposals until the responsible people approve them.

9. Measure the Results

To determine whether AI-assisted planning is actually helping, compare it with your previous planning process.

Useful metrics include:

Time to plan: Time required to produce and approve a migration wave plan.
Inventory completeness: Percentage of in-scope resources with validated ownership, configuration, and dependency data.
Dependency accuracy: Number of missing or incorrect dependencies discovered during testing or migration.
Plan changes: Number of significant changes required after wave approval.
Migration success rate: Percentage of workloads migrated within agreed acceptance criteria.
Downtime: Actual service interruption compared with the approved target.
Cost variance: Difference between estimated and actual AWS operating costs.
Rollback rate: Number of migrations that required a rollback.
Security findings: Issues identified during pre-migration review and post-migration validation.

Use a small pilot to establish a baseline. Do not assume a particular time saving or cost reduction without measuring it in your own environment.

10. A Practical Adoption Strategy

Organizations can introduce AI-assisted migration planning gradually.

Phase 1 — Prepare: Build a reliable inventory, identify application owners, and define business priorities and risk criteria.

Phase 2 — Pilot: Select a low-risk workload and compare the AI-assisted plan with a plan produced by the existing process.

Phase 3 — Validate: Review dependency accuracy, strategy choices, cost assumptions, security controls, and rollback readiness.

Phase 4 — Standardize: Define who can submit data, approve plans, change migration scope, and authorize execution.

Phase 5 — Scale: Expand to additional applications and waves while incorporating lessons learned from completed migrations.

AWS recommends a structured migration journey across assessment, mobilization, and migration and modernization. AI-assisted planning works best when it is part of that broader program rather than a replacement for it.

Migration guidance: https://docs.aws.amazon.com/transform/latest/launchguide/delivering-mig-program.html

Conclusion

AI agents can make AWS migration planning more interactive, structured, and adaptable. By helping teams analyze discovery data, group dependent applications, and develop migration waves, AWS Transform can reduce some of the repetitive work involved in preparing a migration.

But the most important part of migration planning remains the quality of the decisions. Accurate inventory, verified dependencies, business context, security controls, and tested rollback procedures matter just as much as the speed of generating a plan.

My recommended approach is simple: start with reliable data, use AI to develop a draft, validate the recommendations with the people who understand the workloads, and require explicit approval before high-impact changes.

Use AI to accelerate the planning process—not to remove accountability from the migration process.

Official AWS Resources

#AWS #AWSCloud #AWSTransform #AIAgents #CloudMigration #CloudComputing #AWSArchitecture #GenerativeAI #DevOps #CloudModernization

# aws-ambassador # aws-builders # aws-community-builders # agentic-ai # cloud-migration

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Builder Center

AWS AI Agent Migration Planning: Build a Smarter AWS Migration Roadmap with AWS Transform

Muhammad Umair Ashraf

Introduction

1. Why Traditional Migration Planning Is Challenging

2. What Is AWS Transform?

3. How an AI-Assisted AWS Migration Workflow Works

Stage 1: Discover the existing environment

Stage 2: Analyze and group applications

Stage 3: Recommend a migration strategy

Stage 4: Build migration waves

Stage 5: Review, approve, and execute

4. Reference Architecture

5. Hands-On Mini Project: Prepare an Inventory for AI Migration Planning

Project structure

Step 1: Create a sample inventory

Step 2: Create an inventory validation script

Step 3: Run the validator

6. Use an AI Agent to Draft a Migration Plan

7. Example: A Draft Migration Wave Plan

8. Security and Human Approval: Keep the Migration Under Control

Apply least privilege

Protect inventory data

Require explicit approval for high-impact actions

Validate before execution

9. Measure the Results

10. A Practical Adoption Strategy

Conclusion

Official AWS Resources