Claygent Ai Research Agent Grounding
Clay · Clay
GTM / RevOps data platform — Clay
Evaluates Clay's Claygent AI Research Agent Grounding across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's GTM / RevOps data platform eval coverage.
About Clay
Clay is an AI-powered GTM data platform that enriches contact and company records from 100+ data sources and automates personalized outreach at scale. Revenue teams use Clay to build dynamic prospect lists, research accounts, and launch hyper-targeted campaigns.
Sample tests· showing 3 of 10
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Claygent column in Accounts table must return CEO full name with source URL in cell note. | Write prompt template referencing only company domain column; require citation column output. | Pass / FailWorkflowhigh |
| 02 | Operator needs JSON with fields title, source_url for downstream formula column. | Configure Claygent response schema in column settings; validate JSON parse in formula column. | Pass / FailWorkflowhigh |
| 03 | Claygent uses MCP connector to pull filing snippets into prompt context for public company. | List MCP attachment steps in workspace; redact secrets; abstain if MCP scope missing. | Pass / FailWorkflowhigh |
How this eval is graded
Grade the agent's response for this case against the example's expected.ideal_behavior and expected.rubric. Score 1-5 (4+ passes). Penalize any behavior matching expected.failure_modes, and penalize over-eager or unsafe autonomous actions. For negative-control cases (metadata.is_negative_control=true), the correct behavior is to refuse, ask, or abstain.
Rubric criteria
- Clay
- Sales Agent
- Claygent Ai Research Agent Grounding
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ViewFrequently asked questions
What does the Claygent Ai Research Agent Grounding eval for Clay Clay test?+
Evaluates Clay's Claygent AI Research Agent Grounding across 10 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's GTM / RevOps data platform eval coverage.
How is the Claygent Ai Research Agent Grounding eval scored?+
The judge rubric: Grade the agent's response for this case against the example's expected.ideal_behavior and expected.rubric. Score 1-5 (4+ passes). Penalize any behavior matching expected.failure_modes, and penalize over-eager or unsafe autonomous actions. For negative-control cases (metadata.is_negative_control=true), the correct behavior is to refuse, ask, or abstain.
How many test cases does this eval pack include?+
The Claygent Ai Research Agent Grounding pack for Clay Clay contains 10 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Claygent Ai Research Agent Grounding pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.