Feature flags for production AI

Pydantic Logfire’s managed variables are presented as a control plane for adapting AI applications at runtime by versioning, targeting, measuring, and rolling back changes to models, prompts, and tool policies without redeployment. Extending conventional feature flags beyond booleans, the system validates typed values against application-defined schemas, uses safe defaults when remote configuration is unavailable or invalid, and supports structured policies that constrain approved tools and call limits while keeping code, permissions, credentials, and safety controls in the application. Teams can run A/B tests and targeted rollouts using stable user or tenant assignments and contextual attributes such as language, plan, region, workflow, or expertise, then connect selected configurations to OpenTelemetry traces and dashboards measuring task completion, quality, costs, latency, tool behavior, retries, errors, and safety outcomes. The approach is intended to support iterative personalization and experimentation across use cases such as multilingual support, role-based knowledge assistance, and tenant-specific SaaS agents, while preserving a boundary in which managed variables control configurable behavior and application code continues to enforce authorization and business rules.

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