Automate AI agent evaluation with multi-grader systems and statistical analysis.
npx skills add https://github.com/BishopCodes/OpenPAI --skill evals-bishopcodes
Please help me install this Agent Skill. Skill: Evals Source: https://github.com/BishopCodes/OpenPAI/tree/main/skills/Utilities/Evals Command: npx skills add https://github.com/BishopCodes/OpenPAI --skill evals-bishopcodes
This Skill provides a robust framework for evaluating AI agents, ensuring their performance meets predefined quality standards and identifying regressions before they impact users.
Run the evals skill to evaluate the current agent's performance on the core behaviors suite.
High-intent search queries and answers about installing and using this skill.
You can benchmark AI agents by automating their evaluation with a multi-grader system that applies code-based, model-based, and human graders to measure performance against predefined criteria and thresholds.
Regression testing for AI agents evaluates entire interaction workflows over time to identify performance dips, ensuring new versions maintain quality standards and do not negatively impact user interactions.
Capability testing for customer service agents is set up by running an evaluation suite with customizable criteria, verifying that common user queries are handled correctly with accurate information and appropriate tone.
Yes, you can use a multi-grader system for AI quality assurance that combines code-based, model-based, and human graders to comprehensively assess agent interactions and flag any performance regressions.
Agent evaluation tracks performance over time by integrating with verification algorithms and applying statistical analysis, allowing you to monitor workflows and detect regressions across different versions.
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