How to deploy an AI agent from sandbox to production

Deploying an AI agent to production requires more than proving it works in a sandbox, adding governed controls for identity, access, isolation, data handling, repeatable releases, auditing, cost limits, monitoring, and incident response. Organizations should define a production contract covering interfaces, allowed tools, state, resource limits, service objectives, and failure behavior, while keeping authorization outside the model’s discretion and isolating untrusted code or commands. The recommended architecture separates authenticated request handling, orchestration and policy enforcement, sandboxed execution, durable data services, and observability, with versioned artifacts, prompts, policies, tool schemas, and model configurations promoted through testing, staging, and progressive rollouts. Testing should evaluate task quality alongside security threats, permissions, resilience, idempotency, load, and full-stack behavior, while operations should track quality, policy decisions, availability, latency, capacity, and cost. The guide presents Northflank as a platform that combines sandboxing, deployment, workflows, stateful services, private networking, access controls, audit logging, and cloud or bring-your-own-cloud deployment options to support this production lifecycle.

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