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To add real-time AI to a legacy system, modernize the business decision flow in slices rather than moving the application unchanged or replacing it all at once. Start with a capability that needs fresher decisions, route its requests through a controlled boundary, and progressively introduce services and data flows that can support timely inference. Keep batch processing where it remains appropriate, validate old and new behavior during coexistence, and retain a rollback path until the new flow is proven.
Start with the decision, not the cloud migration
A system can run in the cloud and still behave like a batch-era application: tightly coupled components, a centralized database, scheduled jobs, and releases that require coordinated changes. Moving that system without changing its operating model does not make its decisions timely.
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Begin with a concrete business decision that would improve if it used fresher information. Trace what information it needs, how that information becomes available, which application behavior follows, and how quickly a useful action must occur. “Real time” is not a universal latency target; the reviewed sources do not establish a general millisecond service-level objective. Define freshness and response expectations for the specific decision instead.
Prem Kumar Gadhanki frames the architecture question this way: “The better modernization question is not, ‘How do we move this application to the cloud?’” It is, “How should this system operate if intelligent, real-time decisioning becomes a core capability?” (Cloud Native Now, August 25, 2026.)
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Choose which work needs events and which can stay in batch
Event-driven processing is useful when a business reaction depends on a change as it happens. It is not a mandate to convert every workload or make every dataset continuously available. A practical design keeps scheduled work for decisions that do not need immediate action and creates an event path for decisions whose value depends on timely context.
| Approach | Fits when | Design concern |
|---|---|---|
| Batch | Work can wait for a scheduled run, such as periodic processing that does not change an immediate business action. | Keep the cadence and acceptable data age explicit; a batch result may not be suitable for a time-sensitive decision. |
| Event-driven | A change should trigger enrichment, inference, decision logic, and a business action promptly. | Define ownership, data contracts, replay behavior, idempotency, and error handling; a broker alone does not provide these guarantees. |
A useful event path is event → context enrichment → AI inference → decision logic → business action. The inference output should inform a decision, not silently become an action by default: application rules and business controls still determine what happens next.
Migrate one capability at a time with a controlled route
The Strangler Fig pattern lets teams route requests to legacy behavior or replacement services through a façade, then shift functionality in stages. Microsoft describes it as “a controlled and phased approach to modernization.” The pattern preserves a route between old and new behavior during transition; it is not a guarantee of zero downtime or automatic correctness. (Microsoft Learn / Azure Architecture Center, metadata dated May 29, 2026.)
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- Put a façade in the request path. Route the capability through a controlled boundary that can direct traffic to the legacy implementation or its replacement.
- Build the replacement around a stable contract. Keep the new capability’s responsibility bounded. Define how callers request inference or receive an event-based result without binding application behavior to a particular model implementation.
- Integrate the data deliberately. Where the new capability needs a separate data store, plan an initial load, ongoing change-data capture (CDC) synchronization, and validation. Microsoft’s migration guidance describes this sequence; the authoritative data source and handling of conflicts during coexistence must still be explicit for the system being migrated.
- Validate before shifting traffic. Check output behavior and data consistency against agreed criteria. Decide how to route requests back if the replacement is not ready to carry the capability.
- Shift traffic progressively. Move requests in stages while monitoring the complete business flow. Keep the legacy route available as long as it is part of the recovery plan.
- Retire old structures only after dependencies are resolved. Removing legacy database structures is a deliberate later step, not an automatic consequence of introducing CDC or a new service.
Google Cloud’s cloud-native re-architecture material also treats modernization as a re-architecture concern rather than simply a move of infrastructure. The sequence above is a practical synthesis of the migration patterns, not a requirement that every application adopt identical components. (Google Cloud, “Re-architecting To Cloud Native”.)
Keep model serving independent from application behavior
A stable inference API or event boundary separates the model’s serving lifecycle from the rest of the application. That boundary makes it easier to version models, test a candidate in a canary rollout, scale serving independently, measure results, and roll back a model change without requiring a coordinated rewrite of every caller.
Freshness matters as much as response speed. A fast inference request can still produce a poor decision if it uses stale context. Identify which inputs materially change the outcome, how current those inputs need to be, and where they are assembled. Avoid making unrelated enterprise data real time simply because the AI path is event-driven.
Keep decision logic visible and measurable. Record which model version and context informed the decision, and define how uncertain, invalid, delayed, or unavailable results are handled. The architecture should make it possible to distinguish a model-serving problem from stale context or a downstream business-action failure.
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Decompose only where separate ownership or scaling pays off
Service boundaries are valuable when a capability needs independent ownership, deployment, or scaling. Splitting a system into services without those benefits can instead introduce network latency, more distributed failure modes, and extra observability work.
Before extracting a component, ask who owns its data and behavior, whether it needs a different release cadence or scaling profile, and what happens when its dependencies are unavailable. If the answer does not justify independent operation, keeping the component within a larger deployable unit may be simpler. Cloud-native architecture is an operating model and set of design choices, not a service-count target.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Build reliability around the whole decision flow
Reliability has to cover the path from request or event through context access, inference, decisioning, and downstream action. Use timeouts to bound waits; retries with backoff where retrying is safe; circuit breakers where a dependency failure should stop repeated calls; idempotency to prevent duplicate processing from repeating a business action; and dead-letter handling for events that cannot be processed normally.
These controls need to fit the operation. Retrying a non-idempotent action can create duplicate effects, while retrying indefinitely can tie up resources and increase delays. Define what is retried, how many attempts are appropriate for the flow, and how an operator can inspect or replay a failed event.
- Measure end-to-end latency and success for the business flow, not just model inference time.
- Track failures and delays at the gateway, event system, enrichment and context access, inference, decisioning, and downstream action stages.
- Correlate events and requests across service boundaries so a slow or incorrect outcome can be traced to its cause.
- Monitor data freshness and synchronization health alongside application availability.
- Keep replay and recovery procedures consistent with idempotency and the business consequences of repeating an action.
Use Kubernetes as an enabler, not a substitute for architecture
Kubernetes can provide workload scheduling, horizontal scaling, service discovery, health management, and rolling deployments. Those capabilities can support a cloud-native system, but placing a poorly designed application in containers does not create service boundaries, resilient communication, automated delivery, or useful observability.
Choose Kubernetes when its operational capabilities fit the workload and the team can operate the platform. Another execution model may be more appropriate for a particular system. The sources reviewed do not establish a universally superior provider or platform, so the decision should follow workload needs and operational capacity rather than a presumption that cloud-native means Kubernetes.
Make the migration decision against explicit trade-offs
| Decision axis | Question to resolve | Practical implication |
|---|---|---|
| Migration risk and reversibility | Can traffic be shifted gradually, and can each cutover be reversed? | Keep the old route and a tested rollback plan until validation supports retiring it. |
| Data ownership and consistency | Which system is authoritative during coexistence, and how will synchronization lag and discrepancies be detected? | Plan initial load, ongoing synchronization, validation, and a deliberate point for removing old structures. |
| Decision freshness and latency | Which inputs must be current, where is context assembled, and what response time does the business decision require? | Set requirements per flow; there is no universal target established by the reviewed sources. |
| Operational complexity | Will another independent service create meaningful ownership, deployment, or scaling benefits? | Account for extra network calls, distributed failures, and monitoring responsibilities before decomposing. |
| Model lifecycle | How will model versions be tested, observed, released, and rolled back independently? | Use a stable serving boundary between model changes and application behavior. |
| Platform fit | Which runtime and platform match the workload and the team’s operating capability? | Treat Kubernetes and other execution models as options, not prerequisites. |
What a sound first slice looks like
A strong first slice has a named business decision, a clear owner, a bounded capability, and an explicit definition of sufficiently fresh context. It has a façade or equivalent controlled route, a data synchronization and validation plan where needed, a stable model-serving boundary, and a staged traffic change with recovery available. Its success is judged across the decision flow and resulting action—not by cloud deployment or inference speed alone.
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