AI agents for cloud modernization are software systems that analyze your cloud and application environment, plan modernization work, and carry out bounded steps such as inventory, dependency analysis, code or manifest transformation, and proposed infrastructure changes. What they can safely automate depends less on the model than on the authority you give it. Reading, analyzing, and drafting are low-risk. Writing to production, or doing anything irreversible, should stay behind human approval, narrow permissions, and your normal deployment controls.
The three big providers now ship agent-style tooling, but their scope, maturity, and availability differ. Several offerings were still in public preview as of October 2026, so treat this as a map of what exists and how to constrain it, not a green light for any particular workload.
What an AI agent for cloud modernization actually is
A conventional migration tool runs a fixed procedure. An agent is given a goal, such as “assess these servers and propose a migration plan,” and decides which steps and tools to use to get there. It can call tools and APIs that read, and sometimes change, real cloud state. That delegated authority is the source of both the value and the risk: the agent can do useful multi-step work, but a mistake, a poisoned input, or an over-broad permission can have real consequences. Microsoft’s shared-responsibility guidance for AI agents makes this point directly, stressing permission design and authorization of individual actions.
What agents can do across the modernization lifecycle
Microsoft describes its migration agent as working across discovery, assessment, planning, migration, and code transformation. AWS and Google describe narrower, workload-specific examples. The table maps those stages to a sensible default level of autonomy. The right-hand column is an analytical framework built from the providers’ security guidance, not a vendor-published standard.
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| Stage | What the agent typically does | Reasonable default posture |
|---|---|---|
| Discovery and inventory | Enumerates servers, VMs, applications, databases, and dependencies | Read-only access; safe to automate if scoped to the accounts or subscriptions in question |
| Assessment and dependency mapping | Identifies what depends on what and flags migration blockers | Automate the analysis; have humans check the conclusions before they drive decisions |
| Planning | Proposes target architectures, sequencing, and wave plans | Agent drafts; humans review and approve the plan |
| Code and manifest transformation | Rewrites application code or Kubernetes configuration for the target platform | Output goes through code review and tests like any other change |
| Infrastructure changes | Proposes or generates infrastructure definitions | Proposals only, applied through your existing deployment pipeline |
| Migration execution and cutover | Moves workloads and switches traffic | Human approval gate for production-affecting or irreversible steps |
What the major providers offer, and how mature it is
| Offering | Scope described by the provider | Status and approval model |
|---|---|---|
| AWS Transform | Specialized agents for VMware, mainframe, and .NET workloads | AWS describes review and approval of plans, code, and infrastructure suggestions. Availability per workload and region should be checked with AWS. |
| Azure Copilot migration agent (Microsoft) | Servers, VMs, applications, and databases, across discovery, assessment, planning, migration, and code transformation | Described as a public preview in Microsoft’s 2026 announcement. Microsoft says the human stays in control of what to act on and how to validate outcomes. |
| EKS-to-GKE Agentic Migration (Google Cloud) | Kubernetes transitions from AWS EKS to Google Kubernetes Engine | Described as a public preview in Google’s October 5, 2026 Modernize announcement, with built-in human approval gates. |
Two things stand out. First, the scopes barely overlap: one is a multi-workload factory, one is a general migration assistant, and one is a single Kubernetes path. Second, “preview” appears in two of the three descriptions. A preview agent is not evidence that the workflow is ready for every production workload, and you should confirm region, supported versions, and terms for your own case.
“The human stays in control throughout—deciding what to act on and how to validate outcomes.” — Jeremy Winter, Corporate Vice President and Chief Product Officer, Azure Platform, Microsoft Azure blog, 2026.
That is Microsoft’s stated design approach in its own announcement, not an independent evaluation of how any agent behaves in practice.
Rank #2
What you can safely automate, by risk tier
Tier 1: read-only analysis
Inventory, dependency discovery, and assessment are the safest starting point because the agent only needs read permissions. The risk shifts from damage to accuracy. A missed dependency in an assessment can still cause an outage later, so a person should check conclusions that feed a go/no-go decision.
Tier 2: drafting artifacts
Migration plans, transformed code, rewritten manifests, and proposed infrastructure changes are artifacts, not actions. They are safe to generate automatically when they land in a review queue, a branch, or a pull request and pass through the same tests and approvals as human-written work. AWS frames its suggestions this way, with review and approval of plans, code, and infrastructure proposals.
Tier 3: execution in controlled environments
Letting an agent apply changes to a sandbox or non-production environment is a reasonable next step, provided its credentials cannot reach production and its budget and step count are capped. This is where you learn how the agent behaves before trusting it with more.
Rank #3
Tier 4: production and irreversible actions
Microsoft’s guidance calls for human-in-the-loop gates on sensitive, irreversible, or production-affecting operations. Cutovers, data deletion, permission changes, and network or DNS changes belong here. Google’s EKS-to-GKE preview builds approval gates into the workflow, which is the pattern to look for in any product you evaluate.
The controls that make automation safe
These come mainly from Microsoft’s shared-responsibility guidance for AI agents and Google’s May 2026 security update on agent identity and access.
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Give each tool the minimum permissions it needs, use tool allowlists, and check authorization on each action rather than once per session. An agent that only needs to read inventory should never hold a role that can modify networking.
Bounded execution
Microsoft recommends planning limits, loop detection, and budget or cost ceilings. A runaway agent that retries a failing step indefinitely can burn money or repeatedly alter resources.
Untrusted input handling
Modernization agents read a lot of content you did not write: legacy code comments, configuration files, documentation, tickets. Sanitize and validate untrusted content that enters the workflow, and treat messages passed between agents as a trust boundary, not as trusted instructions.
Distinct agent identity and policy enforcement
Google’s May 6, 2026 post describes dedicated agent identities, access management, policy enforcement on agent-to-tool connections, guardrails, and runtime protections. It labels some of these specific capabilities as preview. The principle applies across providers: an agent should act under its own identifiable, revocable identity so its actions can be audited and cut off.
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Clear ownership and a central baseline
Microsoft recommends a centralized, enforceable governance baseline aligned with identity, data governance, and security practices, with named ownership, monitoring, and accountability for each agent.
Know what you own
Microsoft notes that responsibility shifts toward the customer as deployment moves from SaaS to PaaS to IaaS. For agents, it highlights three added areas: the orchestration layer, tools and actions, and memory or state. A managed service inherits more platform controls. A self-built agent on infrastructure you run leaves you responsible for the agent’s logic, its tools, its identity, and its permissions.
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- Workload scope: VMware, mainframe, .NET, Kubernetes, servers, VMs, databases, applications, or code. Match the tool to what you actually run.
- Workflow stage: does it stop at assessment and planning, or also transform code and execute migration?
- Autonomy and approval: what can it read or change without review, what needs sign-off, and how can plans and changes be tested or rolled back?
- Identity and security: per-agent identity, least-privilege tool access, per-action authorization, input validation, audit logs, and runtime monitoring.
- Deployment and responsibility: SaaS, managed PaaS, or self-hosted, and which controls you inherit versus own.
- Availability: public preview versus general availability, plus region, supported versions, integration constraints, and licensing.
This is a framework drawn from provider announcements and security guidance. It is not a provider-neutral benchmark, and no independent cross-provider data on agent accuracy, production incident rates, or typical savings was identified for this article.
How to read the numbers vendors publish
AWS’s Transform announcement includes vendor-reported figures such as “up to 4x faster” and workload-specific savings or speed claims. They are AWS’s own claims, tied to specific workloads and comparisons. They don’t establish what you should expect in your environment, and they are not independent evidence across providers.
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On demand, Microsoft cites a Forrester Q1 2026 Cloud and AI Application Modernization Survey, in which 91% of IT leaders saw application modernization as necessary to enabling AI advancements. The base was 223 global leaders responsible for their organization’s cloud and AI strategy. It is reported through Microsoft’s blog, so it measures sentiment, not whether agents deliver results, and it isn’t independent validation of any product.
Quick Recap
A practical way to start
- Pick a low-stakes workload and begin with read-only discovery and assessment.
- Create a dedicated identity for the agent with only the permissions that stage requires.
- Route every generated plan, code change, and infrastructure proposal into your normal review and testing pipeline.
- Run any execution step in a non-production environment first, with step and cost limits set.
- Define in advance which actions need explicit human approval in production, and confirm the product enforces those gates rather than just suggesting them.
- Turn on logging so every tool call can be traced to the agent identity that made it.
- Expand scope only after the results from the previous stage hold up under review.
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