AI agents for cloud modernization are software systems that can inspect cloud and application environments, plan migration or transformation work, and use connected tools to carry out bounded steps. They can help with tasks such as inventory, dependency analysis, migration planning, and code or infrastructure proposals. They should not be given open-ended authority over production: test their output, restrict their permissions, and require human approval for consequential changes.
What makes an AI agent different from a cloud assistant?
A conventional assistant mainly answers questions or generates suggestions. An agent can also use tools—such as cloud APIs, repositories, or migration utilities—to gather information and take actions within the permissions it has been granted. That makes it potentially useful across multiple stages of modernization, but it also means a mistake can affect real systems if the agent is allowed to make changes.
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“Agent” does not mean fully autonomous, universally capable, or safe by default. A product may analyze and propose a change while leaving execution to a person; another workflow may perform certain steps automatically but pause at approval gates for others. The practical question is not whether a product is called an agent, but what it can read, what it can change, and which actions require review.
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Discovery and assessment
An agent can help collect information about servers, applications, databases, and dependencies, then organize findings for assessment. Microsoft describes its Azure Copilot migration agent as supporting work across discovery, assessment, planning, migration, and code transformation. Its announcement describes support for servers, virtual machines, applications, and databases. These are vendor-described capabilities, not a guarantee that every environment or version is supported.
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Dependency analysis and planning
Agents can help map relationships between components and turn assessment findings into a proposed sequence of modernization work. A useful plan should expose its assumptions and dependencies so engineers can check whether it accounts for shared services, data flows, access requirements, and operational constraints. Planning is safer than execution, but an incorrect plan can still cause costly rework if it is accepted without review.
Code and configuration transformation
Agents can propose or generate changes to application code, deployment manifests, or infrastructure definitions. Treat generated changes as untrusted until they pass the same review, testing, security, and deployment controls used for human-written changes. A syntactically valid manifest or successful build does not by itself establish that the resulting service is secure, reliable, or equivalent to the original.
Migration execution
Some offerings are built around specific source and destination environments rather than general-purpose modernization. AWS says AWS Transform uses specialized agents for VMware, mainframe, and .NET workloads. Google Cloud describes its EKS-to-GKE Agentic Migration as a public preview for moving Kubernetes workloads from Amazon EKS to Google Kubernetes Engine. Google says that preview includes human approval gates.
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How do the named provider offerings differ?
The following is a comparison of provider-described scope, not an independent benchmark. Availability and capabilities can change; the status below reflects the cited provider information available on October 7, 2026.
| Offering | Provider-described workload or stages | Availability stated in the cited material | Approval or autonomy information stated |
|---|---|---|---|
| AWS Transform | Specialized agents for VMware, mainframe, and .NET modernization | The cited AWS material does not establish a preview status in the information summarized here; check current availability and supported workloads with AWS. | AWS describes review and approval of plans, code, and infrastructure suggestions. Do not infer that every action in every workflow requires the same approval. |
| Azure Copilot migration agent | Servers, virtual machines, applications, and databases; stages include discovery, assessment, planning, migration, and code transformation | Public preview, as described in Microsoft’s 2026 announcement | Microsoft describes a human-controlled approach; confirm which operations in the current preview are read-only, proposed, or executable. |
| Google Cloud EKS-to-GKE Agentic Migration | Kubernetes transitions from AWS EKS to Google Kubernetes Engine | Public preview, announced by Google Cloud on October 5, 2026 | Google says the preview includes built-in human approval gates. Confirm the specific gated actions and workflow limits before use. |
Before choosing an offering, verify current region availability, supported versions, integrations, licensing, and preview terms directly with the provider. Preview status is not a general assurance of production suitability.
What can an agent safely automate?
Safety depends on the combination of task impact, reversibility, confidence, and granted authority—not simply on whether the task is labeled “migration.” A reasonable starting policy is to automate observation and low-risk preparation, then increase autonomy only after the workflow has been tested and its controls are proven.
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- Suitable for automation with review: producing migration plans, generating code or manifest changes, and preparing infrastructure proposals. Require a qualified person to inspect the assumptions and resulting changes, then run tests and security checks before deployment.
- Keep explicit approval for high-impact actions: production writes, permission or identity changes, data movement or deletion, cutovers, and other sensitive or difficult-to-reverse operations. Use established change management, deployment, and rollback controls rather than relying on an agent’s own assessment of success.
This is a risk-based operating policy, not a claim that every vendor product supports each level of control. Confirm the actual controls available in the product and workflow you plan to use.
How should you put guardrails around an agent?
Because an agent can invoke tools and APIs, its effective authority is the authority of its identity and connections. Microsoft’s shared-responsibility guidance emphasizes that customers retain more responsibility as deployment shifts from SaaS toward PaaS and IaaS. For agent workflows, that includes responsibility for the orchestration layer, tools and actions, and memory or state. A managed service may provide some platform controls, but it does not remove the need to govern what the agent can access and do.
- Define one bounded job. Specify the workload, environment, allowed actions, success criteria, and stop conditions. Avoid granting broad permissions for a vague goal such as “modernize the estate.”
- Use least privilege at each tool. Give the agent only the permissions needed for its task, preferably with read-only access during discovery. Where changes are necessary, separate identities and permissions for environments or action types rather than reusing an all-powerful credential.
- Authorize actions individually. Check permissions when the agent attempts an action, not only when its overall workflow starts. Use allowlists for tools and actions; do not assume that a trustworthy model makes every tool call safe.
- Limit execution and cost. Set bounds on planning steps, run length, loops, and budgets. Detect repeated or circular actions and stop the workflow when it exceeds its limits.
- Validate inputs and tool output. Treat repository content, tickets, logs, documents, and other externally supplied material as potentially untrusted. Sanitize or constrain it so text embedded in those sources cannot silently expand the agent’s instructions or permissions.
- Require approval for consequential writes. Put a human gate before sensitive, irreversible, or production-affecting operations. The reviewer should see the proposed action and its expected impact, not just a generic “approve” prompt.
- Keep an audit trail and monitor behavior. Record which identity acted, which tools it called, what changed, and who approved the change. Monitor for unexpected access or actions and define how to stop the agent and revoke its credentials.
- Test recovery before production use. Validate outputs in a non-production environment, rehearse rollback or restoration, and verify that the human approval and emergency-stop paths work.
Microsoft’s guidance also treats messages passed between agents as trust boundaries: one agent’s output should not automatically become another agent’s trusted instruction. Google’s May 2026 security update describes dedicated agent identities, policy controls for agent-to-tool connections, access management, guardrails, and runtime protections; the update marks some specific capabilities as preview. Verify the availability and scope of those controls rather than assuming they are present in every deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you evaluate an agent for your environment?
Compare the product and your operating model together. A promising demo is not enough to establish that an agent fits a particular workload or can be safely operated.
- Workload fit: Does it support your actual source and target platforms, application stack, versions, and integration constraints?
- Stage fit: Does it only discover and plan, or can it also transform code, create infrastructure, or execute migration steps?
- Control maturity: Can you see proposed changes, test them, require action-level approval, restrict tools, and roll back safely?
- Identity and accountability: Can each agent have a distinct identity with narrowly scoped permissions, and can your team trace its decisions and actions?
- Deployment responsibility: Which security and governance controls are provided by the service, and which must your team build and operate?
- Availability and cost: Is the feature generally available or in preview in your region, and what usage, licensing, or implementation costs apply to your case?
Provider announcements are useful for understanding intended features, but they do not establish independent comparisons of accuracy, incident rates, or the savings a customer should expect. AWS publishes speed and savings claims for some workloads; treat these as AWS-reported claims with their stated workload and comparison context, not as cross-provider evidence or a forecast for your environment. Measure your own pilot against a defined baseline.
When is an agent a poor fit?
An agent is a poor fit when the target workload is unsupported, the required permissions cannot be restricted, the proposed changes cannot be meaningfully reviewed or tested, or the organization lacks a clear owner for monitoring and rollback. It is also risky to use a preview for a critical production migration without confirming its documented limits and having a tested recovery plan.
In those situations, use the agent only for bounded analysis—or do not use it for that workflow. Modernization decisions still require people who understand business constraints, application behavior, security obligations, and operational risk.
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