EKS-to-GKE migration needs a source-to-target parity gate

Define parity at the workload boundary

Begin with one source-to-target contract for each candidate workload rather than approving an agent for an account, cluster, or migration program. Record the EKS cluster and Kubernetes version, namespace, workload owner, business service, container images and digests, manifests, Helm or Kustomize inputs, custom resources, operators, admission controls, secrets, service accounts, IAM relationships, storage classes, persistent volumes, network policies, ingress, service discovery, DNS, certificates, load balancers, autoscaling, disruption budgets, topology rules, observability, backup, recovery objectives, and cost baseline. Name every external dependency and every object that is intentionally excluded. The contract should identify the authoritative source for each configuration and the person who can decide whether the GKE representation is equivalent, acceptably different, or unresolved.

Define parity as an evidence question, not a text comparison. Some objects will translate directly, some will require a Google Cloud service or policy, and some will remain outside the automated path. Preserve the source object, generated target proposal, tool and model version, transformation rule, assumptions, warnings, manual edits, reviewer, approval, deployed object, and resulting runtime state. A syntactically valid manifest can still change scheduling, identity, network reachability, storage behavior, scaling, failure handling, or support responsibility. Require an exception state when the system cannot establish the target meaning; do not let a plausible manifest or a successful dry run silently stand in for an architectural decision.

Test translation and approval as one controlled change

Use a representative set that includes ordinary Deployments, StatefulSets, DaemonSets, jobs, sidecars, init containers, custom resources, privileged or host-dependent workloads, multiple storage patterns, strict network policy, workload identity, autoscaling, regional dependencies, and an unsupported case. Run discovery twice from a pinned source snapshot and compare the outputs. Confirm which credentials the migration capability can read, how they are supplied and cleared, which repositories and clusters it may write, how branch and pull-request protections apply, and whether every generated change can be traced to a source object and approved rule. Human-in-the-loop language is useful only when the approver can see the complete change, relevant dependencies, unresolved warnings, and downstream effect before authorization. ^[1]

Exercise denial and change-control paths deliberately. Remove an approval, change the source after discovery, revoke a credential, introduce an unsupported controller, alter a storage class, create a network-policy conflict, and submit a target edit outside the declared GitOps route. The system should stop, identify the stale or unauthorized proposal, and require a new review rather than continuing with an earlier snapshot. Preserve discovery time, source commit or export, generated commit, policy checks, reviewer comments, approvals, deployment identity, and reconciliation result. Verify that generated configuration cannot bypass environment promotion, separation of duties, policy-as-code, vulnerability review, or emergency-change controls already required for manually authored infrastructure.

Prove data, traffic, and operating parity under failure

Build a test matrix from the service objectives and difficult states the workload is expected to survive. Compare source and target startup, readiness, response correctness, throughput, tail latency, error handling, identity decisions, network allow and deny outcomes, storage semantics, persistence, ordering, backup and restore, autoscaling, rescheduling, zone loss, dependency failure, certificate rotation, observability, alert routing, and operator access. Reconcile data counts, keys, checksums where meaningful, timestamps, permissions, retention, and rejected or in-flight work. A green deployment and a sample response do not prove equivalent customer behavior, durable state, incident visibility, or recovery.

Treat traffic transition as its own governed object. Record the eligible population, routing layer, DNS and load-balancer state, session behavior, data-write authority, replication lag, health signal, increment size, observation window, stop threshold, decision owner, and exact route back to EKS. Test shadow, canary, partial, and full cutover paths only where the application architecture supports them. Introduce a target regression, delayed dependency, bad policy, failed scale event, partial region loss, and rollback while writes are active. Confirm which data and requests must be reconciled after reversal and who can freeze, drain, replay, or correct them. The migration is not accepted until operators can reconstruct what moved, what differed, who approved it, and how service and state were restored.

Release the preview by evidence, not portfolio label

Start with a bounded, reversible workload whose owners can observe both source and target and whose data path permits safe testing. Pin the Google capability and documentation available at the time, source cluster snapshot, repositories, policy bundle, target cluster and add-ons, test data, traffic profile, accountable owners, acceptance thresholds, and expiry date. Stop when discovery coverage is incomplete, generated changes cannot be explained, identity or network policy broadens, data parity is unresolved, service measures regress, logs cannot reconstruct the action, target cost exceeds the approved bound, or rollback is untested. Keep the source environment and a manually usable recovery path until the organization has explicitly accepted the retirement conditions. ^[1]

The CIO should require application, platform, cloud, security, identity, network, data, resilience, FinOps, procurement, legal, compliance, and business-service owners to sign the scoped result and remaining exceptions. Reopen the record when the preview changes, supported objects or regions expand, a source or target cluster version changes, an operator or custom resource is added, identity or networking is redesigned, traffic or data characteristics change, pricing or support terms change, or an incident exposes a parity gap. Google Cloud Modernize is a provider portfolio; it is not a buyer's migration decision. Production authority comes from source-to-target evidence that survives translation, runtime, failure, cutover, and rollback. ^[1]

Turn this source into a reviewable decision

For AI for CIOs, use this briefing as a dated decision record rather than a substitute for the source. Preserve Google Cloud, Introducing Google Cloud Modernize, transforming for (and with) AI, the exact URL, the October 6, 2026 review date, the supported facts above, the editorial interpretation, the limitations, and any buyer-specific evidence. Link that record to the decisions most directly affected: Software delivery and modernization; Enterprise AI platform architecture; Operations and incident intelligence; Data products and AI-ready information. State whether the source changes the scope, evidence requirement, control, sequence, or only the language used to describe the decision.

Before action, name the accountable owner, affected population and workflow, exact offering or configuration, source data and rights, human decision point, exception and appeal path, complete cost, expected benefit, failure and stop conditions, retained evidence, and next review date. Keep official facts, provider statements, buyer observations, representative tests, measured outcomes, editorial inferences, and unknowns visibly separate. Reopen the record when the source, offer, model, integration, data, policy, population, responsible person, or measured result changes.

Limitations and unknowns

Google Cloud is the provider and sole source for this October 5, 2026 announcement. Its official RSS feed recorded the item at 16:00 UTC on October 5, and the page's publication metadata records 18:00:03 -0500 on October 5; both timestamps are after the prior successful-run cutoff, so the development is treated as post-cutoff current intelligence. EKS-to-GKE Agentic Migration is described as public preview, and the public announcement does not independently establish entitlement, supported regions or versions, object and controller coverage, translation correctness, credential behavior, GitOps enforcement, policy equivalence, data integrity, network behavior, service continuity, security, compliance, reliability, performance, price, cost, support, rollback, or production outcome for a buyer. Verify current documentation and terms, authorized source and target environments, complete discovery output, generated and manual changes, representative normal and adverse runtime tests, data and traffic reconciliation, observability, cost, stop and rollback evidence, and qualified application, platform, cloud, data, security, identity, network, resilience, finance, procurement, legal, compliance, accessibility, and business-owner review before production reliance.