Why HTTP-based evals worked better for our AI team than SDK-only setups (www.getmaxim.ai)

The Maxim platform has announced enhancements to its evaluation process by introducing HTTP Endpoint-Based Offline Evaluations, a feature designed to ease friction in evaluating AI agents. Traditionally, evaluations tied to codebases limited accessibility, requiring developers to set up local environments and execute scripts. The new HTTP Endpoint capability allows teams to connect complex AI agents to the platform via a standard API, transforming evaluations into a one-click action through a user-friendly interface. This innovation significantly enhances collaboration, enabling Product Managers and domain experts to run evaluations independently, thereby speeding up the feedback loop and accelerating the development lifecycle. Moreover, the introduction of the {{simulation_id}} variable for multi-turn conversation simulations simplifies the orchestration of testing scenarios, maintaining contextual continuity without complex coding. This architecture not only promotes secure handling of authentication with Maxim Vault but also allows large organizations to standardize quality across various independently developed agents. By using HTTP Endpoint-Based Evals, teams can ensure consistent performance and safety standards are met before deploying agents into production, making this a transformational approach for both small developers and large enterprises in the AI/ML community.