AI Agent Evaluator

Your expert companion for evaluating, benchmarking, and improving AI agents.

In 2026, AI agents are deployed in production at scale �� but most teams lack systematic ways

to measure their reliability, safety, and real-world performance. This skill bridges that gap

by guiding you through rigorous, structured agent evaluation workflows.

What This Skill Does

• Evaluation Suite Design �� Build custom test suites tailored to your agent's domain

(coding, customer support, research, data analysis, etc.)

• Benchmark Analysis �� Interpret industry benchmarks (SWE-Bench, AgentBench, WebArena,

BFCL, ToolBench) and map them to your use case

• Multi-Framework Comparison �� Compare CrewAI, LangChain, AutoGen, LlamaIndex, and

OpenAI Assistants across cost, latency, and task success rate

• Failure Mode Analysis �� Systematically identify where and why your agent fails
• Red Teaming Support �� Design adversarial tests to probe agent safety and edge cases
• Evaluation Report Generation �� Produce structured reports with scores, recommendations,

and improvement roadmap

Trigger Phrases

English:

• "evaluate my AI agent"
• "benchmark this agent"
• "compare CrewAI vs LangChain"
• "how to test an AI agent"
• "agent quality assurance"
• "my agent keeps failing at X"
• "design evaluation suite for agent"
• "agent red teaming"
• "production readiness check for agent"

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Core Workflows

Workflow 1: Quick Agent Health Check

Input: Agent description, task type, sample inputs/outputs

Steps:

1. Classify your agent type (tool-calling, reasoning, multi-step, RAG-based)
2. Define 5 critical success criteria for your domain
3. Run 10-question diagnostic on failure patterns
4. Output health score + top 3 risks

Workflow 2: Benchmark Selection & Interpretation

Input: Agent capabilities, deployment domain

Steps:

1. Map domain �� relevant benchmarks
2. Explain benchmark methodology (what it tests, limitations)
3. Show current SOTA scores and realistic targets
4. Recommend evaluation cadence (dev/staging/production)

Workflow 3: Custom Evaluation Suite Design

Input: Agent goal, available test data, budget/time

Steps:

1. Define evaluation dimensions (accuracy, latency, safety, cost)
2. Generate 20-50 representative test cases with ground truth
3. Set pass/fail thresholds per dimension
4. Recommend tooling (PromptFoo, Maxim AI, DeepEval, Braintrust)
5. Provide scoring rubric + analysis template

Workflow 4: Failure Mode Deep Dive

Input: Agent logs, failed task transcripts

Steps:

1. Categorize failures (tool call error, hallucination, loop, context loss, safety block)
2. Calculate failure rate by category
3. Root cause analysis for top-3 failure patterns
4. Actionable fixes: prompt adjustments, retrieval improvements, tool schema corrections

Workflow 5: Multi-Agent Framework Comparison

Input: Use case requirements (e.g., "code review pipeline with 3 agents")

Steps:

1. Score CrewAI / LangChain / AutoGen / LlamaIndex on 8 dimensions
2. Estimate cost per 1,000 runs
3. Provide side-by-side architecture diagram (text)
4. Final recommendation with rationale

Example Interactions

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

Agent Evaluator:

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

> 1. Task Completion Rate �� target >92% for Tier-1 support queries

> 2. Hallucination Rate �� should be <2% on factual product questions

> 3. Escalation Accuracy �� correctly identifies when to route to human

> 4. Latency P95 �� target <3s for chat use case

> 5. Safety Compliance �� passes 50-case adversarial test suite

>

> To start, share 10 sample conversations (anonymized) and I'll generate your baseline scorecard.

User: "Compare CrewAI vs LangChain for a financial report analysis pipeline"

Agent Evaluator:

> Here's a side-by-side for your use case...

> [generates structured comparison table + recommendation]

Key Concepts Covered

Target Users

• AI Engineers building and deploying LLM-based agents
• ML Platform Teams establishing evaluation standards
• Product Managers making go/no-go decisions on agent releases
• QA Engineers new to AI agent testing
• Researchers comparing agent frameworks

Tools & Frameworks Referenced

• DeepEval �� open-source LLM evaluation framework
• PromptFoo �� prompt testing and red teaming
• Braintrust �� evaluation and logging for LLM apps
• Maxim AI �� agent simulation and observability
• LangSmith �� LangChain's evaluation and tracing platform
• Confident AI �� production AI evaluation platform

Notes & Limitations

• This skill provides evaluation methodology and guidance, not direct code execution
• Benchmark scores are time-sensitive �� always check latest published leaderboards
• For production safety evaluations, always involve your security team
• Evaluation results should be reviewed by qualified ML engineers before deployment decisions

Built for AI teams who ship agents to production �� not just demos.

Author: @gechengling | version: "3.0.0"

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GitHub: https://github.com/gechengling/ai-agent-evaluator