agent-artifex:assess

An AI integration design assessment evaluates tool descriptions, parameter design, error handling, and system prompts to identify quality issues. It diagnoses why agents make incorrect tool selections and maps testing coverage against critical server and conversational behaviors.

Incorrect tool selections frequently stem from unclear tool descriptions, flawed parameter design, or inadequate system prompts. An agent design audit identifies these specific issues in your LLM integration and highlights testing gaps in tool discovery and agent behavior.

Assess testing coverage for LLM integrations by mapping current tests against critical areas like tool discovery, agent behavior, server correctness, and chatbot conversational flow. This process identifies untested paths and delivers a prioritized gap analysis with actionable recommendations.

A prioritized gap analysis for AI integration design includes a ranked list of improvements based on impact and effort. It covers design principles and testing methodologies, delivering actionable recommendations for tool descriptions, error handling, and system prompts.

Yes, you can evaluate conversational design and system prompts without external dependencies. The assessment operates directly on your existing project files to identify design quality issues and testing gaps in your AI services.