Why HTTP-based evals worked better for our AI team than SDK-only setups

Enhancing AI Evaluation Efficiency: Transitioning from SDK-Only to HTTP-Based Endpoints

In the rapidly evolving field of artificial intelligence, efficient evaluation processes are vital for accelerating development, ensuring quality, and enabling broader team involvement. Our experience suggests that reliance solely on SDK-based evaluation methods, while functional, introduces significant limitations that can hinder progress. This article explores how transitioning to HTTP endpoint-based offline evaluations transformed our workflow, leading to increased agility and collaboration.

The Limitations of SDK-Only Evaluation Approaches

Historically, our AI evaluation workflows depended on integrating scripts directly within our codebase through SDKs. While this method provided a straightforward mechanism to test models, it proved to be slow and somewhat restrictive. Each evaluation required considerable engineering effort—merging branches, configuring environments, and manually orchestrating scripts. Consequently, product managers and domain experts found themselves blocked from running their own evaluations, creating bottlenecks and reducing iteration speed.

Adopting HTTP Endpoint-Based Evaluation for Greater Flexibility

To overcome these challenges, we transitioned to using HTTP endpoints for performing offline evaluations. This approach decouples the evaluation logic from the agent’s core code, instead exposing the agent as a RESTful API. The evaluation process is now orchestrated through a dedicated interface, managed by a platform that allows team members to trigger evaluations via a user-friendly UI—akin to a “Postman for AI”—rather than writing or modifying test scripts.

Key Benefits of the New Approach

Implementing HTTP endpoint-based evaluations has delivered several significant improvements:

• Empowered Product Managers and Stakeholders: Teams can now initiate evaluation runs directly on staging or production agents without reliance on engineering support, fostering greater autonomy.
• Accelerated Iteration Cycles: Rapid feedback on prompt adjustments or flow iterations enables more effective experimentation and refinement.
• Simplified Regression Testing: Automated testing within continuous integration (CI) pipelines ensures consistent performance checks without manual intervention.
• Streamlined Multi-turn Conversation Scripting: State management complexities diminish, making it easier to script and test multi-turn dialogues effectively.

Managing State and Security Concerns

For stateful agents, our platform manages session contexts using generated simulation IDs—eliminating the need for fragile client-side session management logic. Additionally, secrets and authentication credentials are securely handled through a vault service, ensuring safe internal testing environments.

Is Your Evaluation Process Due for an Upgrade?

If your team relies exclusively on SDK scripts for AI evaluations, it might be time to