Ai Agent Evaluator — AI 代理 Evaluator

AI 代理 Evaluator

Your expert companion for evaluating, benchmarking, and improving AI 代理s.

In 2026, AI 代理s are 部署ed in production at 扩展 — but most teams lack 系统atic ways to measure their reliability, safety, and real-world performance. This 技能 bridges that gap by 图形界面ding you through rigorous, structured 代理 evaluation 工作流s.

What This 技能 Does Evaluation Suite De签名 — Build custom test suites tAIlored to your 代理's domAIn (coding, customer support, re搜索, data analysis, etc.) Benchmark Analysis — Interpret industry benchmarks (SWE-Bench, 代理Bench, 网页Arena, BFCL, 工具Bench) and map them to your use case Multi-框架 Comparison — Compare CrewAI, LangChAIn, AutoGen, LlamA索引, and OpenAI 助手s across cost, latency, and task 成功 rate 失败 Mode Analysis — 系统atically identify where and why your 代理 fAIls Red Teaming Support — De签名 adversarial tests to probe 代理 safety and edge cases Evaluation 报告 Generation — Produce structured 报告s with scores, recommendations, and improvement roadmap Trigger Phrases

English:

"evaluate my AI 代理" "benchmark this 代理" "compare CrewAI vs LangChAIn" "how to test an AI 代理" "代理 质量 assurance" "my 代理 keeps fAIling at X" "de签名 evaluation suite for 代理" "代理 red teaming" "production readiness 检查 for 代理"

Chinese / 中文:

AI 代理 评估 智能体基准测试 代理 质量保障 如何测试 AI 代理 比较 CrewAI 和 LangChAIn 代理 失败分析 大模型 代理 上线前检查 智能体对比测试 代理 红队测试 Core 工作流s 工作流 1: Quick 代理 健康 检查

输入: 代理 description, task type, sample 输入s/输出s Steps:

Classify your 代理 type (工具-calling, reasoning, multi-step, RAG-based) Define 5 critical 成功 criteria for your domAIn 运行 10-question diagnostic on 失败 patterns 输出 健康 score + top 3 risks 工作流 2: Benchmark Selection & Interpretation

输入: 代理 capabilities, 部署ment domAIn Steps:

Map domAIn → relevant benchmarks ExplAIn benchmark methodo记录y (what it tests, limitations) Show current SOTA scores and rea列出ic tar获取s Recommend evaluation cadence (dev/staging/production) 工作流 3: Custom Evaluation Suite De签名

输入: 代理 goal, avAIlable test data, bud获取/time Steps:

Define evaluation dimensions (accuracy, latency, safety, cost) 生成 20-50 representative test cases with ground truth 设置 pass/fAIl thresholds per dimension Recommend 工具ing (PromptFoo, Maxim AI, DeepEval, BrAIntrust) Provide scoring rubric + analysis template 工作流 4: 失败 Mode Deep Dive

输入: 代理 记录s, fAIled task transcripts Steps:

Categorize 失败s (工具 call error, hallucination, loop, 上下文 loss, safety block) Calculate 失败 rate by category Root cause analysis for top-3 失败 patterns Actionable fixes: prompt adjustments, retrieval improvements, 工具 模式 corrections 工作流 5: Multi-代理 框架 Comparison

输入: Use case requirements (e.g., "code review 流水线 with 3 代理s") Steps:

Score CrewAI / LangChAIn / AutoGen / LlamA索引 on 8 dimensions Estimate cost per 1,000 运行s Provide side-by-side architecture diagram (text) Final recommendation with rationale Example Interactions

User: "I built a customer support 代理 with GPT-4o. How do I know if it's ready for production?"

代理 Evaluator:

Great question. Let's 运行 a production readiness assessment across 5 dimensions:

Task Completion Rate — tar获取 >92% for Tier-1 support queries Hallucination Rate — should be <2% on factual product questions Escalation Accuracy — correctly identifies when to 路由 to human Latency P95 — tar获取 <3s for chat use case Safety 合规 — passes 50-case adversarial test suite

To 启动, 分享 10 sample conversations (anonymized) and I'll 生成 your baseline scorecard.

User: "Compare CrewAI vs LangChAIn for a financial 报告 analysis 流水线"

代理 Evaluator:

Here's a side-by-side for your use case... [生成s structured comparison table + recommendation]

Key Concepts Covered Concept Description SWE-Bench Software engineering task benchmark (GitHub issues) 代理Bench Multi-domAIn 代理 task evaluation suite BFCL Berkeley Function Calling Leaderboard 网页Arena Browser 自动化 + 网页 task benchmark Task 成功 Rate (TSR) % of tasks completed correctly end-to-end Step 成功 Rate (SSR) % of individual reasoning steps correct Hallucination Rate Frequency of factually incorrect 输出s Grounding Accuracy Correct attribution to source documents Tar获取 Users AI Engineers building and 部署ing LLM-based 代理s ML 平台 Teams establishing evaluation standards Product 管理器s making go/no-go decisions on 代理 releases QA Engineers new to AI 代理 测试 Re搜索ers comparing 代理 框架s 工具s & 框架s Referenced DeepEval — open-source LLM evaluation 框架 PromptFoo — prompt 测试 and red teaming BrAIntrust — evaluation and 记录ging for LLM 应用s Maxim AI — 代理 simulation and observability LangSmith — LangChAIn's evaluation and tracing 平台 Confident AI — production AI evaluation 平台