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Overview
Claygent is Clay's AI research agent—a feature that lets you run autonomous research tasks directly within Clay tables. Instead of manually researching each prospect, Claygent can visit websites, analyze content, and extract specific information at scale. For GTM teams already using Clay for enrichment, Claygent extends those capabilities with AI-powered research.
What we'll cover:
- What Claygent is and how it differs from standard Clay enrichments
- Core capabilities: web research, content analysis, and custom extraction
- Setup and prompt engineering best practices
- Real limitations and credit considerations
- How Claygent fits into a broader GTM workflow
What is Claygent?
Claygent is Clay's built-in AI agent for custom research. While Clay's standard enrichment columns pull structured data from providers (Apollo, Clearbit, etc.), Claygent can perform open-ended research tasks that don't fit into predefined fields.
Think of the difference this way: standard enrichments answer "What is this company's employee count?" Claygent can answer "What does this company's pricing page say about their enterprise tier?"
Core Capabilities
| Capability | What It Does | Example Use Case |
|---|---|---|
| Web Research | Visits URLs and extracts specific information | Pull pricing info from prospect websites |
| Content Analysis | Reads and summarizes web content | Summarize company blog posts or press releases |
| Custom Extraction | Finds specific data points based on prompts | Identify tech stack from job postings |
| Multi-Step Research | Chains multiple lookups together | Find CEO, then research their background |
For teams building sophisticated ABM plays with Clay, Claygent adds the research layer that structured enrichments can't provide.
How Claygent Works
Basic Workflow
- Add a Claygent column to your Clay table
- Write a prompt describing what you want researched
- Reference other columns (company URL, name, etc.) as context
- Claygent runs the research for each row
- Results populate the column
Prompt Structure
Effective Claygent prompts typically include:
- What to find: Specific information you need
- Where to look: URLs or search strategies
- How to format: Expected output structure
- What to return if not found: Fallback behavior
Claygent uses Clay credits per execution. Complex research tasks that require multiple web visits consume more credits than simple lookups. Monitor credit usage, especially when running Claygent across large tables.
Example Prompts
Find recent funding:
"Visit {company_website} and search for recent press releases or news. Find any funding announcements from the past 12 months. Return the funding amount, round type, and date. If no funding found, return 'No recent funding found.'"
Analyze tech stack from careers page:
"Visit {company_website}/careers and analyze job postings. Identify technologies, tools, and platforms mentioned. Return a comma-separated list of technologies found."
Best Use Cases for Claygent
Competitive Intelligence
Research competitors mentioned on prospect websites, analyze their positioning, or identify which solutions they currently use. This informs competitive positioning in outreach.
Personalization Research
Find specific details for personalized outreach: recent blog posts, press mentions, product launches, or executive quotes. This enables personalization that goes beyond basic firmographics.
ICP Qualification
Research criteria that structured data doesn't cover: Does this company sell to enterprises? Do they have an existing integration with X? Are they hiring for roles that signal growth? This supports qualification beyond standard enrichment.
Content Intelligence
Analyze prospect content: What topics do they write about? What pain points do they mention? What language do they use? This informs messaging strategy.
Event and Signal Detection
Monitor for specific events: leadership changes, product launches, office expansions, or partnership announcements that create outreach opportunities.
Setting Up Claygent Effectively
Start with Clear Objectives
Define exactly what information you need and how you'll use it. Vague objectives lead to vague prompts and inconsistent results.
Write Specific Prompts
Be explicit about what to look for, where to look, and how to format output. Include fallback instructions for when information isn't found.
Test on Small Batches
Before running Claygent across thousands of rows, test on 10-20 records. Refine prompts based on output quality before scaling.
Monitor Credit Usage
Complex research burns credits quickly. Set up monitoring and alerts to avoid unexpected credit depletion.
Validate Output Quality
Spot-check Claygent results for accuracy. AI research can hallucinate or misinterpret content. Human validation on samples is essential.
Honest Limitations
Accuracy Variability
Claygent interprets web content using AI, which means results aren't always accurate. Complex pages, dynamic content, or ambiguous information can lead to errors. Always validate before acting on Claygent output.
Credit Costs
Research-intensive prompts consume significant credits. At scale, Claygent can become expensive. Balance depth of research against credit budget.
Rate Limits and Timeouts
Complex research or sites with anti-bot measures can cause timeouts or failures. Not every research task will succeed for every row.
No Persistent Context
Each Claygent execution is independent. It doesn't remember previous research or build knowledge over time. For context that needs to persist, you need external systems.
Claygent excels at finding information but doesn't know your positioning, ICPs, or messaging strategy. Teams getting the most from Claygent combine it with context systems like Octave. Claygent finds the data; your context layer determines what to do with it—which persona it maps to, which pain points are relevant, which messaging applies.
Claygent vs. Alternatives
| Approach | Best For | Limitations |
|---|---|---|
| Claygent | Custom research within Clay workflows | Credit costs, accuracy variability |
| Standard Clay enrichments | Structured data (firmographics, contact info) | Can't do custom research |
| Manual research | High-value accounts needing precision | Doesn't scale |
| Custom scrapers | Repeatable, high-volume extraction | Engineering required |
Claygent fits best when you need custom research at moderate scale with Clay workflow integration. For very high volumes or precise requirements, other approaches may be more cost-effective.
Integrating Claygent into GTM Workflows
Claygent is most powerful as part of a larger workflow:
Enrichment → Research → Qualification → Outreach
- Standard enrichment: Pull firmographics, contacts, tech stack from data providers
- Claygent research: Add custom research (recent news, competitive intel, specific signals)
- Qualification: Score and segment based on combined data using AI qualification rules
- Outreach: Push qualified, enriched records to your sequencer
This workflow leverages Claygent for what it does best—custom research—while using structured enrichment for efficiency and downstream tools for action. For complete workflow guidance, see Clay research to qualification to sequences.
Frequently Asked Questions
Credit consumption varies by task complexity. Simple lookups use fewer credits; multi-step research with multiple web visits uses more. Check Clay's current pricing documentation for specifics.
No. Claygent can only access publicly available web content. Gated content, login-required pages, and private data are inaccessible.
Accuracy varies by task and content complexity. For critical use cases, validate Claygent output through sampling. Don't rely on AI research without verification for high-stakes decisions.
Yes, but validate first. AI-generated research can contain errors. Using incorrect information in outreach damages credibility. Review Claygent output before incorporating into customer-facing content.
Conclusion
Claygent extends Clay's capabilities from structured enrichment to custom AI research. For GTM teams needing information that standard data providers don't offer—competitive intelligence, specific signals, content analysis—Claygent fills the gap.
But Claygent is a research tool, not a strategy tool. It finds information; it doesn't know what that information means for your GTM motion. For that strategic layer—which personas this maps to, which messaging applies, how to prioritize—you need additional context.
Teams combining Claygent's research capabilities with centralized GTM context from tools like Octave get the best of both: AI-powered research that feeds into strategic, context-aware workflows.