1. Describe the intended use
Specify the problem the component is meant to address and where its output enters a workflow. A documentation assistant, administrative routing service, imaging integration, and clinical recommendation can have very different review responsibilities.
State who will use the output and what decisions it may influence. Architecture planning does not validate a model's clinical effectiveness or establish permission to use it. Appropriate evaluation and approval must be defined for the particular use case.
2. Map the permitted data path
Identify the originating system, permitted inputs, hosting location, inference endpoint, and destination for the result. Include logs, retained inputs, service accounts, and the way errors are handled. Determine which interfaces the existing EHR or application actually supports.
Use synthetic inputs for initial discovery and reference demonstrations. Before a live integration, agree on data access, handling, retention, and the review process with the responsible owners. A convenient endpoint is not a substitute for an approved boundary.
3. Document the infrastructure requirements
Hosting and inference requirements should be visible alongside identity, network, observability, and recovery needs. Specify which settings can be represented in infrastructure-as-code and which require configuration or acceptance within the existing application.
Record what the integration partner supplies and what the client team retains responsibility for. This includes credentials, deployment access, service operations, supported adapters, and evidence that the chosen pattern fits the environment.
4. Define the review path
Describe when a result can be used, when it requires human review, and when it must be held as an exception. Keep the input and output context available to the designated reviewer. State who is responsible for resolving uncertainty or conflicting information.
The review process should be appropriate to the workflow. A model response or confidence indicator is not a clinical guarantee. If the use case involves clinical decisions, define the relevant evaluation and approval responsibilities separately from the infrastructure review.
5. Plan the unavailable and rollback paths
Decide whether the component is optional or essential to the workflow. Establish the response to an unavailable endpoint, rejected request, invalid output, or a required dependency becoming unavailable. Specify what is queued and what returns to the established non-AI process.
Agree on the conditions for disabling or rolling back the integration, the owner of that decision, and the evidence required to resume it. The continuity plan should be part of the architecture brief rather than added after a successful demonstration.
6. Evaluate one integration at a time
Start with one category and a bounded pilot. Review the reference architecture, exercise synthetic normal and exception inputs, and record the unresolved questions. Use measurable acceptance criteria that reflect the intended workflow rather than claims about a model in general.
SynArch's modular AI expansion direction proposes architecture and infrastructure support for integration partners. Vendor adapters, plug-in IaC generation, policy overlays, and drift detection are scope-dependent planned capabilities. An evaluation should establish what can be delivered before making a production commitment.