Running an AI agent on company servers: production architecture
Running a model locally is only one component of an on-premises agent; knowledge, tools, identities, audit evidence and safe failure behaviour complete the system.
Draw the real boundary
Locate the interface, model, documents, search index, tools, logs and backups, and identify every telemetry, update or external API path. Assign owners for the data, workflow and operation.
Document update, telemetry and identity dependencies alongside model hosting; an external call changes the claimed operating boundary.
Build inspectable layers
Use an authenticated gateway, pinned model service, permission-aware retrieval, narrow tool adapters, event records and infrastructure monitoring. Add write actions individually with confirmation and replay protection.
Version model, retrieval index, prompts and tool contracts together so an incident can be reproduced against the released configuration.
Accept a workflow, not a demo
Test normal, ambiguous, forbidden and adversarial cases, unavailable dependencies and cross-user data attempts. Begin with one team and retain evidence sufficient to reconstruct each action.
Acceptance should cover unavailable services, hostile documents, cross-user requests and repeated operations, not only correct answers.
Continue with the underlying material
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