AI Model Management in Production: A Practical Workflow

Managing AI features that rely on hosted models requires a lifecycle distinct from conventional code deployment because teams must frequently adjust model selection, prompts, parameters, targeting, and safeguards without redeploying applications. The described approach centers on externalizing these decisions into versioned runtime configurations, evaluating proposed variations against representative and adversarial datasets with calibrated LLM-based judges, and releasing changes gradually to targeted audiences while monitoring quality, cost, latency, token usage, and errors. Guarded rollouts and automatic rollback can limit the effects of regressions, while production evaluation samples live traffic to identify issues not captured in pre-production testing. Experimentation complements quality evaluation by measuring whether model changes improve business outcomes such as conversion or task completion, with consistent variation assignment needed for reliable attribution. The article presents LaunchDarkly AgentControl as an integrated implementation of these capabilities, combining managed model and prompt configurations, playground-based evaluation, audience targeting, rollout controls, monitoring, adaptive fallbacks, and experimentation, while emphasizing that production failures should become future test cases in a continuous feedback loop.

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