Testing and Optimizing Agents with AgentKit

As AI agents move from prototype to production, performance optimization becomes critical. AgentKit's Evals component provides developers with sophisticated tools to measure, analyze, and improve agent performance systematically.

Understanding Agent Evaluation

Traditional software testing approaches don't fully capture the nuances of AI agent behavior. AgentKit's Evals addresses this challenge with purpose-built evaluation tools designed specifically for autonomous agent systems.

Step-by-Step Trace Grading

Trace grading helps identify where agents might be making suboptimal decisions, hallucinating information, or failing to utilize available tools effectively. By visualizing the agent's decision tree, developers can pinpoint exact failure modes and optimize accordingly.

One of the most powerful features of Evals is step-by-step trace grading. This capability allows developers to examine each decision point in an agent's workflow, understanding not just the final output but the reasoning process that led to it.

Component-Level Datasets

Rather than evaluating entire agent workflows as monolithic black boxes, Evals provides datasets for assessing individual components. This granular approach enables targeted improvements without requiring full system rewrites.

Automated Prompt Optimization

Developers can create custom evaluation datasets that reflect their specific use cases, ensuring that optimization efforts align with real-world requirements rather than generic benchmarks.

Evals includes automated prompt optimization that continuously tests variations of prompts against evaluation datasets. This feature can dramatically reduce the manual iteration cycles typically required to fine-tune agent behavior.

The system learns which prompt structures, formats, and instructions produce the best results for specific tasks, automatically incorporating these insights into production agents.

External Model Evaluation

A unique feature of AgentKit Evals is the ability to run evaluations on external models directly from the OpenAI platform. This capability allows developers to compare different model providers, versions, and configurations without building separate testing infrastructure.

Best Practices

Start with comprehensive baseline evaluations before making changes. Establish clear success metrics that align with business objectives. Use A/B testing to validate improvements before full rollout. Continuously monitor production performance and iterate based on real-world data.