Quick Answer: What Is Claygent?
Claygent is Clay's AI research agent. It visits websites in real time, reads pages like a human would, and returns structured answers to specific questions about companies or people. Think of it as a research assistant that runs inside your Clay tables, filling in the gaps that enrichment APIs cannot cover.
What Claygent Actually Does
If you have used enrichment tools, you know the pattern: you pass in a domain or email, and you get back structured fields like company size, industry, or funding stage. That works until you need something the database does not track. Does this company serve enterprise customers? What pain points show up in their job postings? Are they a Salesforce shop or a HubSpot shop based on their integrations page?
Claygent fills that gap. It takes a research question and a target (usually a company domain or LinkedIn URL), opens the relevant pages, reads them, and returns an answer. The mental model is straightforward: it is a web scraper with an LLM brain that can follow instructions instead of just extracting fixed fields.
The Core Capabilities
Claygent has three main modes of operation:
- Basic web research. Give it a domain and a question ("What industries does this company serve?"), and it visits the website, reads the relevant pages, and returns a structured answer. This is the bread and butter use case.
- Navigator mode. For sites that require interaction, Claygent can click buttons, apply filters, fill out search forms, and scrape data from pages that do not make information accessible through simple page loads. Useful for directories, job boards, and gated content previews.
- MCP connections. You can connect Claygent to first-party data sources like Gong transcripts, Salesforce opportunities, or Google Docs. This lets you pull internal context into your research prompts, so Claygent can write outreach emails using your actual tone guidelines or reference real deal context.
The Builder Interface
One feature that matters operationally: Claygent has a builder interface where you can write prompts, test them against sample data, and iterate without burning credits. This is important because prompt engineering for research tasks is genuinely hard, and getting it right often takes three or four tries. The ability to test before running on your full table saves real money.
How Claygent Works Inside Clay
Understanding the credit system matters because Claygent can get expensive if you use it carelessly.
Clay's March 2026 pricing overhaul introduced a dual credit system. Data Credits cover enrichment lookups from Clay's 150+ providers. Actions cover platform operations like workflow steps, AI calls, and CRM pushes. Claygent consumes Actions, and a single research task typically uses 5-15 credits depending on how complex the question is and how many pages Claygent needs to visit.
At current rates, that works out to roughly $0.15-1.12 per lead when you combine Claygent with other enrichment steps. Not expensive for high-value accounts, but it adds up fast if you are running Claygent on thousands of rows without good targeting.
Complex Claygent tasks can take 3+ prompt iterations and 50+ credits before you get clean results. Always test in the builder first, and start with small batches when running new prompts on production data.
Where Claygent Sits in a Workflow
In a typical Clay outbound workflow, Claygent usually runs after initial enrichment but before qualification and messaging. The pattern looks like this:
- Pull leads from a source (LinkedIn Sales Navigator, a CSV, an existing CRM list)
- Run standard enrichment to get firmographics and technographics
- Use Claygent to answer specific research questions that enrichment cannot cover
- Score and qualify based on the combined data
- Generate messaging using the research context
The key insight is that Claygent is a research layer, not a decision layer. It gathers facts. Something else needs to interpret those facts and decide what they mean for your GTM motion.
Where Claygent Shines
After running Claygent across a few thousand accounts, patterns emerge for where it works well.
| Use Case | Why Claygent Fits | Expected Accuracy |
|---|---|---|
| Finding target customers from case studies | Claygent can read case study pages and extract customer names, industries, and use cases that no enrichment API tracks | 70-80% for bounded queries |
| Job posting analysis | Identify tech stack decisions, growth signals, and operational priorities from careers pages | High for specific questions |
| ICP qualification questions | Answer yes/no questions like "Do they serve enterprise customers?" or "Do they have a self-serve product?" | 70-80% with good prompts |
| Personalization research | Pull recent news, product launches, or leadership changes for outreach context | Varies by source quality |
| Competitive intelligence | Check which competitors a company integrates with or mentions on their site | High for specific lookups |
The pattern across these use cases: Claygent excels at specific, bounded questions where the answer exists somewhere on the public web. Ask it to find a CEO name from a company website, and it will get it right most of the time. Ask it for open-ended strategic analysis, and results get inconsistent.
Where Claygent Falls Short
Claygent has real limitations that matter for how you architect workflows around it.
JavaScript-Heavy Sites
Pages with heavy JavaScript rendering sometimes fail to load properly. Single-page applications, interactive dashboards, and sites with complex lazy loading can return incomplete or missing data. There is no reliable workaround except testing on sample rows first.
Open-Ended Research
Claygent works best on bounded, specific tasks. "Find the CEO name" works. "Tell me everything interesting about this company" does not. User reports suggest 70-80% accuracy on specific queries, but that drops significantly for open-ended research. The more precisely you can define what you want, the better the results.
Speed at Scale
Processing time can surprise teams used to instant API enrichment. Community reports mention 500 rows taking hours to process for complex queries. This is not a bug; Claygent is actually visiting pages and reading them, which takes time. Plan your workflows accordingly.
It Is Research, Not Strategy
This is the most common mistake teams make with Claygent: expecting it to be a GTM strategy engine. It can tell you facts about a company. It cannot tell you how to position against them, which pain points matter most, or what your messaging angle should be. That interpretation work still needs to happen somewhere, and Claygent will not do it reliably.
Think of Claygent as a junior researcher who is good at following specific instructions and terrible at making judgment calls. Give it clear tasks, verify its work, and do not ask it to think strategically.
How to Get Good Results
Teams that get value from Claygent follow a few consistent patterns.
A Reliable Rollout Pattern
Pick a single research question tied to a real workflow outcome. "Does this company have a self-serve pricing page?" is better than "Research this company's go-to-market model."
Use 5-10 sample companies to validate that your prompt returns useful, consistent results. Iterate until accuracy feels reliable.
Before running on your full list, test on 50-100 rows. Check results manually. Catch issues before they cost real credits.
Structure outputs to include both the answer and the reasoning. "Yes, they serve enterprise (source: pricing page mentions enterprise tier)" is more useful than just "Yes."
Do not auto-enroll leads into sequences based solely on Claygent output. Use it to surface and prioritize, then have humans validate before high-touch motions.
Fitting Claygent Into Your Stack
Claygent works best when paired with clear ICP definitions and messaging frameworks that exist outside of Clay. The research it gathers is raw material. Something needs to interpret that material and decide what it means.
For teams running lead scoring in Clay, Claygent outputs become inputs to scoring logic. The workflow surfaces a signal, the scoring model weights it, and downstream systems act on the result.
For teams focused on personalization, Claygent research feeds into message generation. But the message framework, the value propositions, the proof points, all of that context needs to come from somewhere. Teams that bake all of that into individual Clay prompts end up with brittle workflows that break when messaging changes.
The practical pattern is to treat Claygent as the research layer and pair it with a context layer that knows how your company actually sells. Claygent finds the signal. The context layer decides what the signal means.
Frequently Asked Questions
What is Claygent used for?
Claygent is Clay's AI research agent that gathers contextual information about companies and people by visiting websites, scraping public data, and synthesizing findings into structured outputs. It handles research questions that standard enrichment APIs cannot answer, like whether a company serves enterprise customers or what pain points appear in their job postings.
How much does Claygent cost per query?
Claygent consumes Clay Actions under the dual credit system introduced in March 2026. A single research task typically uses 5-15 credits depending on complexity, which translates to roughly $0.15-1.12 per lead when combined with other enrichment steps. Complex tasks can require multiple prompt iterations, which increases cost.
Is Claygent the same as a Clay AI agent?
Claygent is Clay's AI research agent. Teams sometimes call it a Clay AI agent, but its specific role is research and data gathering inside Clay workflows. It is not an end-to-end GTM automation tool or a strategy engine.
What are the limitations of Claygent?
Claygent can struggle with JavaScript-heavy pages, performs better on bounded specific tasks than open-ended research (roughly 70-80% accuracy on specific queries), and processing 500 rows can take hours. It also requires clear prompting and iteration to get reliable results.
Can Claygent replace manual account research?
Claygent can automate a significant portion of account research, but human review remains important for high-value accounts. It works best for specific, repeatable research questions rather than strategic account analysis. For AI research agents generally, the pattern is automation for volume and human judgment for stakes.
How do I get started with Claygent?
Claygent is available in Clay's Pro tier and above. Start with the builder interface to test prompts without spending credits, begin with specific bounded questions, and validate on small batches before scaling. Clay offers a 14-day free Pro trial to test the functionality.
Conclusion
Claygent is a research agent, not a magic wand. It excels at extracting specific information from unstructured sources when you give it clear instructions and reasonable scope.
The best use cases are ones where the information exists publicly but would take too long to find manually: specific page content, executive details, tech stack signals, recent announcements. The worst use cases are open-ended subjective judgments where you want the AI to do your strategic thinking for you.
Start with bounded questions. Validate outputs on small batches. Use conditional execution to control costs. And remember that Claygent produces research inputs, not final decisions. The qualification logic and messaging strategy still need to come from somewhere else.
