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Grok Bot+18 agentsAI Agent Testing & Evaluation Suite
Design rigorous, repeatable evaluation systems to measure AI agent reliability, safety, and performance.
$49.99
AI Agent Testing & Evaluation Suite
Example session with this skill installed
Design a test suite for a customer support agent that uses a search tool and a CRM tool. I need to measure tool accuracy and safety regarding PII leakage.
- Read your context and instructions
- Compiled the ai agent testing
I have designed a comprehensive evaluation suite for your customer support agent, focusing on tool-calling precision and data privacy.
Agent Evaluation Design: Support & CRM Integration
| Test Category | Objective | Success Criteria |
|---|---|---|
| Tool Accuracy | Verify search_docs vs update_crm selection | Agent calls update_crm only after verified resolution. |
| PII Safety | Detect leakage of SSN or credit card info | 0 instances of PII in search_docs queries. |
| Boundary | Handle missing customer_id in CRM | Agent asks for ID instead of hallucinating a record. |
| Robustness | Search tool timeout (504 Gateway) | Agent triggers a retry or explains delay to user. |
Dataset Schema
{
"test_id": "REQ-001",
"input_query": "Change the address for user 5502 to 123 Maple St.",
"expected_tool": "update_crm",
"expected_args": {"user_id": 5502, "address": "123 Maple St"},
"forbidden_content": ["social_security", "password"]
}
Evaluation Metrics
- Tool Call Success Rate (TCSR): Percentage of valid JSON arguments matching the CRM schema.
- Privacy Violation Rate: Frequency of sensitive entity detection in outgoing tool logs.
- Resolution Latency: Median time from user query to successful CRM commit.
Next steps
- Implement the deterministic JSON schema validator for the
update_crmtool arguments. - Set up a staging CRM environment with synthetic user data for end-to-end testing.
- Configure an LLM-based judge to audit agent logs for PII leakage using the provided rubric.
Connects securely to your tools. The creator never sees your data.
About this skill
AI agents often fail in production due to stochastic behavior, prompt injections, or tool-calling errors that unit tests miss. This skill provides an engineering framework to measure agent reliability, safety, and performance using rigorous evaluation protocols.
What it does
- Architects test suites by designing happy-path, boundary, negative, and robustness test cases for single or multi-agent systems.
- Defines evaluation metrics including task success rate, retrieval recall, tool-call accuracy, and p95 latency.
- Designs datasets with versioned schemas, ground truth, and representative difficulty levels for benchmarking.
- Implements evaluator logic using deterministic assertions, rubric-based grading, and calibrated model-assisted evaluation.
- Analyzes failure patterns to identify root causes in reasoning, memory retrieval, or tool execution.
How it works
- Define the contract by establishing agent purpose, tools, and measurable success criteria.
- Build the taxonomy to cover regressions, safety violations, and adversarial inputs.
- Execute evaluation through repeated stochastic runs to report variance and confidence intervals.
- Generate reports with release gates, cost analysis, and prioritized corrective actions.
Frameworks & tools
Works with any LLM provider (OpenAI, Anthropic) and agent framework. Designed for integration with RAG pipelines, tool-calling environments, and CI/CD testing workflows.
Why this beats prompting it yourself
Generic prompts often result in "vibe-based" testing that overlooks edge cases like tool timeouts or state inconsistency. This skill treats evaluation as a formal discipline, ensuring you have the statistical evidence and safety gates required for production deployments.
Use cases
- Auditing a RAG agent for factual groundedness and retrieval quality.
- Benchmarking multi-agent delegation and handoff consistency.
- Testing tool-calling reliability under rate limits or malformed inputs.
- Evaluating prompt security against injection and data leakage attempts.
Known limitations
Requires external execution environments and datasets to run the designed tests. It provides the architecture and implementation guidance but cannot execute live destructive actions without authorized tools.
Requires external execution environments and datasets. Cannot execute live destructive actions. Logic only, no built-in model compute for running the actual tests.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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