Moving VMware to AWS Just Got a Whole Lot Easier

WILLIAM OBIANA — Tue, 30 Dec 2025 13:40:44 +0000

If you’ve ever tried moving systems from VMware to AWS, you probably know it’s not simple. There’s a lot to figure out such as what servers you have, how applications talk to each other, how the network is set up, and what might break if you move the wrong thing first. Most of this work has traditionally been manual, slow, and prone to error.

AWS is trying to change that with a new service called AWS Transform.

In plain terms, this tool uses AI and automation to take much of the hard work out of migrating VMware environments to AWS.

What Is AWS?

AWS Transform for VMware is a service that helps companies analyze, plan, and prepare their legacy environments (in this context - VMware ) for AWS, automatically.

Instead of spending weeks or months documenting servers, mapping networks, and planning migrations in spreadsheets, this service does much of that work for you.

Think of it like having a smart assistant that:

Looks at your VMware setup
Understands how everything is connected
Creates a clear plan to move it to AWS

What Problems Does It Solve?

1. No More Guessing What You Have

Many companies don’t have a clear picture of all their servers and applications. AWS Transform automatically discovers:

Virtual machines
Applications
Dependencies between systems

2. Smarter Migration Plans

Instead of moving things randomly, the service helps you understand:

Which applications depend on others
What should be moved first
What can be moved together

This reduces downtime and avoids broken systems after migration

3. Automatic Network Conversion

One of the hardest parts of moving from VMware to AWS is rebuilding the network.

AWS Transform can translate your existing VMware network setup into an AWS-ready network design. It even produces ready-to-use AWS templates, so you don’t have to build everything from scratch.

This saves time and avoids configuration mistakes.

4. Flexible that makes you Stay in Control
You don’t have to use everything at once.

You can choose to:

Just assess your environment
Convert only the network
Convert servers and networks
Or run an end-to-end workflow

It works whether you want full automation or just help with specific parts.

5. Works with AWS Migration Tools
AWS Transform integrates with AWS Application Migration Service, so you can:

Test migrated servers in AWS
Track migration progress
Switch over when you’re ready

This makes the move smoother and less risky.

Why This Matters

For years, moving from VMware to AWS has been seen as:

Too complex
Too slow
Too risky

AWS Transform helps remove many of those barriers by:

Automating repetitive tasks
Reducing human error
Speeding up decision-making

For businesses with large or complex VMware environments, this can mean faster migrations, lower risk, and less stress for IT teams.

Conclusion

AWS Transform isn’t about replacing people, it’s about removing the boring, manual work so teams can focus on real decisions and improvements.

If your organization is thinking about moving away from VMware or modernizing infrastructure on AWS, this tool makes the journey simpler, clearer, and much more manageable.

How to Build a Safe and Trustworthy AI Support Agent with Amazon Bedrock AgentCore

WILLIAM OBIANA — Mon, 29 Dec 2025 14:11:35 +0000

AI agents are quickly moving from cool demos to real business tools. But once you let an AI agent talk to customers or connect to your systems, one big question comes up:

Can I trust it?

In this post, we’ll walk through a real-world use case and show how Amazon Bedrock AgentCore helps you build AI agents that are useful, controlled, and safe to run in production.

An AI Customer Support Agent

Imagine you want an AI agent to help with basic customer support, like:

Checking order status
Resetting passwords
Answering common questions
Creating support tickets

You want it to reduce workload for your human agents, but you don’t want it doing anything risky.

Rules

No refunds
No access to payment details
No changing customer data
Escalate to humans when unsure
Be polite, accurate, and helpful

This is exactly the kind of problem Amazon Bedrock AgentCore is designed to solve.

High-Level Architecture

The Agent Brain (Amazon Bedrock)

At the center is Amazon Bedrock, which provides the large language model (LLM).

This is what allows the agent to:

Understand customer questions
Reason about what to do next
Respond in natural language

But the model does not act on its own because AgentCore is in charge of what happens next.

Controlling What the Agent Can Do (Policy Controls)

This is where trust starts.

Before the agent can call any tool or API, AgentCore checks a policy.
If the policy says “no,” the action is blocked.

Example of what the Agent Is Allowed to Do:

Look up order status
Reset passwords
Create support tickets

Example of what the Agent Is NOT Allowed to Do:

Issue refunds
Access payment systems
Modify customer accounts

Example of a Cedar Policy

Cedar is AWS’s policy language used by AgentCore.

Here’s an example:

permit (
  principal,
  action in [
    Action::"getOrderStatus",
    Action::"resetPassword",
    Action::"createSupportTicket"
  ],
  resource
);

And here’s a deny rule for sensitive actions:

forbid (
  principal,
  action in [
    Action::"issueRefund",
    Action::"accessPaymentInfo"
  ],
  resource
);

If the agent tries to refund an order it gets Blocked.
If it tries to access card data it gets Blocked.
The agent is forced to choose a safe alternative, like escalating to a human.

How Actions Flow at Runtime

Here’s what happens when a customer asks a question:

user asks a question
agent decides what action to take
agent checks cedar policy
if allowed, then agent calls endpoint
if denied, then agent adjusts response

This makes the agent’s behavior predictable and auditable.

Making Sure the Agent Is Doing a Good Job (Evaluations)

AgentCore supports episodic memory, which lets the agent learn from experience.

For example:

If users often get confused after a password reset, the agent can add clearer steps.
If certain issues always end in escalation, the agent can do that earlier.

This improves the experience without retraining the model.

Natural Conversations (Real-Time Streaming)

With bidirectional streaming, the agent can:

Respond faster
Be interrupted mid-sentence
Support voice conversations

This makes it feel less robotic and more human, especially for live support.

Conclusion

AI Agents are no longer just experiments, they are becoming real teammates in customer support, operations, and decision-making. But for it to be truly useful in production, it must be predictable, safe, and measurable.

By combining clear policy controls, continuous quality evaluations, and built-in memory, AgentCore gives you confidence that your AI agent will:

Only do what it’s allowed to do
Deliver consistent, high-quality responses
Improve over time without losing control

Instead of relying on hope or manual oversight, you define the rules upfront and verify performance continuously.

Forem: WILLIAM OBIANA

Moving VMware to AWS Just Got a Whole Lot Easier

What Is AWS?

What Problems Does It Solve?

Why This Matters

Conclusion

How to Build a Safe and Trustworthy AI Support Agent with Amazon Bedrock AgentCore

An AI Customer Support Agent

Rules

High-Level Architecture

The Agent Brain (Amazon Bedrock)

Controlling What the Agent Can Do (Policy Controls)

Example of a Cedar Policy

How Actions Flow at Runtime

Making Sure the Agent Is Doing a Good Job (Evaluations)

Natural Conversations (Real-Time Streaming)

Conclusion