Claygent: 5 Clay AI Use Cases for B2B Outbound (2026)

Our inbox gets 10 to 20 cold emails a week that open with a first name followed by a stray middle initial, copied from a LinkedIn profile where the initial is there on purpose. Claygent, Clay’s AI research agent, is the tool that would have caught it.

Every one of those emails says the sender looked at our website or our LinkedIn. The “B.” at the end of the greeting tells us, two words in, that nobody looked at anything.

At The GTM Engineering Company we’ve built Claygents in client tables since 2024, for teams with real pipeline targets. This guide covers what Claygent does, the five agents we run most in outbound, how Clay bills each run, and one job where a plain data provider beat it on cost and accuracy.

Claygent is Clay’s AI research agent. You give it one specific objective. It searches the web, finds the answer and writes it back to your table in a structured format you can filter, score and write copy with. We use it to qualify accounts, find LinkedIn profiles, match company names to domains, clean up copy variables and write first lines.

Key Takeaways (TL;DR)

• Claygent answers one question per row and returns a structured output. Standard enrichment returns general fields like industry and headcount from a database.

• Clay drafts the prompt for you. Reading and rewriting that draft is where the quality comes from.

• Every Claygent run costs one Action plus Data Credits that vary by model. With your own OpenAI, Anthropic or Gemini key, the run costs one Action and zero Data Credits, according to Clay’s pricing page.

• Test on 10, then 50, then 100 rows, and price the full run before you touch a 10,000-row list.

• Use Claygent where no database has the answer. Use a data provider where one already does.

Claygent: at a Glance

What is Claygent, and how is it different from standard enrichment?

Claygent is an agent inside Clay that researches one thing per row and hands the answer back in a format you choose. Clay describes Claygents as agents that “take inputs, follow your instructions, and write a structured output” on its Claygent page.

Standard Clay enrichment for companies and contacts pulls from databases. You get industry, headcount, role, seniority and location. Every competitor with a Clay seat can buy the same fields, so they rarely tell you which account to call first.

A Claygent goes to the web with a single objective. “How many locations does this practice have?” is a Claygent question. So is “Does this company sell to hospitals, true or false?” No firmographic vendor answers either one well, and both decide who gets a message.

How to build a Clay agent

Building one takes a few minutes. You describe what you want in plain language and Sculptor writes the draft. According to Clay’s Claygent Builder docs, the draft defines the prompt logic, sets up the input variables, formats the output and creates test cases. You can also start from a blank editor or from Clay’s templates for prospecting, account scoring, contact scoring and copywriting.

We read every line of the draft Sculptor writes and check it for the four parts a good agent prompt needs: context, the objective, output instructions and examples. Then we cut what’s vague, add constraints and change the output format until it matches what the next column needs. In our builds, that review accounts for most of the difference between an agent that works on 10 rows and one that works on 10,000.

The same docs list three model families you can swap between without touching the prompt: frontier models such as Sonnet 5 and GPT 5.6, open-weight models such as Kimi K2.6 and GLM-5.2, and Clay-managed models such as Neon and Argon. The model you pick changes the cost per row, which matters later in this guide.

Clay is an enrichment and workflow tool, and it doesn’t replace your CRM. Claygent writes to Clay tables, and we push the results into HubSpot or Salesforce from there. If you want the bigger picture of how those tables chain together, our guide to Clay automation walks through it, and our piece on how to automate CRM data enrichment covers the CRM side.

Five Claygent use cases we run for B2B outbound

These five agents show up in most of our client tables. Each one exists because no database answered the question cleanly.

If you’d rather start from a working structure than a blank prompt box, our Clay workflow templates cover several of these patterns.

1. Qualifying accounts no database can

For a client selling to medical practices, we built an agent that visited each practice’s website and answered two questions: how many locations, and how many doctors?

We ran it across every practice in the client’s database. Outbound then went first to practices with several locations and several doctors, because one closed deal there was worth far more than a single-location, single-doctor office. A headcount field from a data vendor would have lumped both types together.

2. Finding LinkedIn profiles

Give the agent a first name, last name and company. It searches Google or LinkedIn for the matching person and returns a profile URL with a confidence level of high, medium or low.

We drop the low-confidence rows before spending anything else on them. For the rest, the profile opens up a second layer of enrichment: awards, languages spoken, how many roles and companies the person has held, and their current title. A rep who knows a VP speaks Spanish and changed companies four months ago writes a different first email.

3. Turning company names into domains

Clients often hand us a list of company names and ask us to find people there. In B2B, a name alone is unreliable, because dozens of companies can share one.

This agent takes the name (plus city or industry when we have it) and returns the company domain or company LinkedIn URL. Once every row carries a domain, we run the same waterfall, the same scoring model and the same copy across the whole list on one consistent key.

4. Normalizing every variable in your copy

The simplest agents are often the most valuable ones in the table. We normalize first name and company name on every variable that appears in a message:

• Capitalization fixed, so “ACME CORP” becomes “Acme”

• Legal suffixes removed, so “Acme Inc.” and “Acme LLC” both become “Acme”

• Middle names and initials stripped from the first name field

Messy source data is common. In Salesforce’s State of Sales report, only 35% of sales professionals said they completely trust the accuracy of their organization’s data.

Skip this step and your team ends up sending the stray-initial greetings from the top of this article. A normalizer costs a fraction of a cent per row and protects every other column you paid for.

5. Writing first lines that show you did your homework

An agent can summarize a prospect’s latest LinkedIn post or the company homepage into one opening sentence. The email then reads like someone did the research first, because the agent did.

We always give this agent a way out. If there’s nothing specific to say, it returns “NONE,” and the row falls back to a standard opener instead of a forced compliment about a post from 2023.

How to control Claygent hallucinations

AI lets outbound scale, and it fails in predictable ways: invented answers, empty runs when an input is missing, and outputs that don’t fit the structure the next column expects. We design around those failures before the first run.

The risk is well documented. In McKinsey’s State of AI in 2025 survey, 51% of respondents from organizations using AI reported at least one negative consequence, and nearly one-third of all respondents reported consequences from AI inaccuracy.

Treat prompts like code. Add constraints and define the exact output format before you run anything. Tell the agent which page to check, what to return and what to write when it can’t find the answer. “Return NOT FOUND” beats a confident guess.

Prefer true/false outputs. When researching a company or contact, make the output a checklist of true/false fields: “has more than one location,” “is hiring SDRs.” Booleans are easy to filter, they plug straight into conditional logic for who gets a message, and a wrong one is easy to spot in review.

Test in stages. Run 10 rows, then 50, then 100, before you touch 5,000 or 10,000. Read every output in the first batch by hand. Claygent Builder lets you test up to 10 inputs at a time, and Clay’s docs say test runs don’t cost credits, so the first round is free.

Give the agent every input it needs. Most of our empty runs trace back to a blank domain or a missing company name upstream. A filter that skips rows without the required inputs stops the agent from guessing and stops you paying for a run that can’t succeed.

Clay AI pricing: how a Claygent run is billed

A Claygent run costs one Action plus Data Credits, and the Data Credit amount depends on the model you select. That’s how Clay’s Claygent page describes it: “1 action per AI prompt,” with fixed Data Credit pricing for Clay’s own models and variable pricing for advanced reasoning models.

The two meters do different jobs. According to Clay’s docs on Actions and Data Credits, Actions measure “the orchestration you do in Clay: enriching data, running AI research, and sending data to other tools,” and each one costs “a few tenths of a penny.” Data Credits pay for data or AI bought from third-party vendors inside Clay. Clay’s pricing page adds that 80% of its models still cost a flat number of Data Credits per task, while token-heavy models are billed on actual token use.

Here are the plans as listed on Clay’s pricing page today:

The FAQ further down the same page lists Launch “starting at $185/mo” and Growth “starting at $495/mo,” and gives Launch 2,500 Data Credits. Check the plan card for the billing period you actually select before you budget. Our full Clay pricing and credits breakdown covers the plan details.

Price the run before you scale it

Unstructured web research is the expensive kind of Claygent. Measure the average cost per row in your 100-row test, multiply it by the size of your database, and ask whether the data is worth that number.

A hypothetical: if 100 test rows consume 40 Data Credits, a 10,000-row run consumes about 4,000. That’s more than the 3,000 monthly Data Credits on the Launch plan card, from one column.

Bring your own API key

Connect your own Anthropic, OpenAI or Gemini key and the run stops drawing Data Credits. Clay’s pricing page states: “Each run will count as one Action, but no Data Credit will be used.” The model provider then bills you for tokens directly. For unstructured web research at scale, we run Claygents on an Anthropic key. Here’s how to save Clay credits with your own API keys.

The same logic works outside Claygent. For one client, a social media company moving from B2C to B2B on Salesforce, we classified 18,000 leads for about $38 by routing the classification through OpenAI instead of Clay credits.

When a data provider beats a Clay AI agent

Claygent is overkill when a data provider already sells the answer, and it’s the cheaper option when no database has it. Decide per use case, with a test run and a cost estimate in hand.

A client wanted Instagram and YouTube follower counts for every company on their list. We built a Claygent for it. It was expensive, it hallucinated often, and it still didn’t return what we needed.

Then we found a data provider, Influencer Club, that took a company domain and returned every social handle (Instagram, YouTube and Twitter) with follower counts for about half a credit per row. One lookup replaced several Claygents at a fraction of the cost. We have no relationship with Influencer Club.

The reverse happens about as often: no vendor sells “number of doctors per practice,” and no vendor cleans your merge fields for you. In those cases a Claygent is far cheaper than any workaround.

Choosing between the two is part of what a GTM engineer is paid for, and it should happen per use case rather than out of habit. Adding agents by default doesn’t add output. Gartner’s November 2025 sales prediction expects AI agents to outnumber human sellers tenfold by 2028, while fewer than 40% of sellers will say those agents improved their productivity. Sometimes the right call is to leave Clay for that step entirely. Our Clay vs Claude Code comparison covers when a script beats a table.

What Claygent-style research did for an enterprise sales team

For a 5,000+ employee enterprise RPA company, AI-driven account research in Clay cut research time from 2.5 hours to under 15 minutes per account. That works out to about 250 hours saved per rep per quarter.

Research is one reason reps sell so little. Reps in the sixth edition of Salesforce’s State of Sales report, a 2024 survey of 5,500 sales professionals, said they spend 70% of their time on non-selling tasks.

Before the build, each enterprise SDR researched about 100 accounts a quarter by hand, across separate documents. We replaced that with one research table in Clay that enriched every account with firmographic and contextual data, sorted job titles into seniority levels and detected whether to personalize in English or Spanish. Reps now open one research document per account instead of piecing together several.

The full build is in the enterprise RPA company case study. For more examples of this pattern in production, see our list of companies using Clay.

Everything You Need to Know About Claygent

Work with us on your Clay build

Most teams we talk to already pay for Clay and a CRM. What’s missing is someone who builds the agents, tests them on 100 rows before 10,000, and wires the answers into HubSpot or Salesforce.

That’s the work The GTM Engineering Company does. We build inside your CRM in weekly working sessions with your team, so you watch every Claygent and workflow go in. Each one ships with a written SOP and a Loom walkthrough, and we design for cost from day one: own API keys, staged tests and a price per row before anything scales. We’re members of Clay’s Expert Program and featured on Clay’s site as vetted experts.

We’re built for VC-backed B2B SaaS teams from post-seed to Series C, roughly $1M to $30M ARR, that have a motion working and need the system behind it. Engagements run 3 or 6 months at $5,000 to $7,000 per month, CRM audit included. By day 30 you have a prioritized fix list, a working enrichment table and at least one live signal flowing into your CRM.

If your Clay bill keeps climbing or your first lines still carry stray initials, start with the audit.

Request the 30-day audit

FAQs About Claygent

What is Claygent in Clay?

Claygent is Clay’s AI research agent, which takes inputs from a table row, follows one instruction and writes a structured answer back to a column. It searches the web for answers no database sells, such as how many locations and doctors a medical practice has. Each run costs 1 Action plus Data Credits that depend on the model you choose.

What can you do with Clay AI for B2B outbound?

With Clay AI for B2B outbound you can qualify accounts from their websites, find LinkedIn profile URLs, match company names to domains, normalize first names and company names, and write first lines from recent posts. The GTM Engineering Company runs these five Claygents in client outbound tables. For one 5,000+ employee enterprise client, Claygent-style research cut account research from 2.5 hours to under 15 minutes per account.

How do you stop Claygent from hallucinating?

You stop Claygent from hallucinating by treating prompts like code: add constraints, define the exact output format and tell it to return “NOT FOUND” when it can’t find the answer. True/false outputs help, because a wrong boolean is easy to spot and filter. Test on 10, then 50, then 100 rows before running 5,000 or more, and read every output in the first batch.

Can I use my own OpenAI or Anthropic API key in Clay?

Yes, you can use your own OpenAI, Anthropic or Gemini API key in Clay. According to Clay’s pricing page, each run then counts as 1 Action and uses 0 Data Credits, and the model provider bills your tokens directly. We use an Anthropic key for unstructured web research at scale.

Is Claygent expensive?

Claygent costs a fraction of a cent in Actions per run, and the total bill depends on Data Credits and list size. Each run costs 1 Action, which Clay prices at a few tenths of a penny, plus Data Credits that vary by model. A test that uses 40 Data Credits per 100 rows becomes about 4,000 credits on a 10,000-row list, which is more than the 3,000 monthly credits on Clay’s Launch plan card.

How much does Clay AI cost?

Clay AI costs $167 per month on the Launch plan card and $446 per month on the Growth plan card, according to Clay’s pricing page in October 2026. The FAQ on the same page lists $185 and $495 as starting prices, so confirm the billing period you select. A Free plan includes 500 Actions and 100 Data Credits per month, and Enterprise pricing is custom.

How is Claygent different from ChatGPT?

Claygent differs from ChatGPT because it runs the same instruction across every row of a Clay table and writes each answer to a column you can filter, score and push to a CRM. ChatGPT answers one conversation at a time, so researching 1,000 accounts means 1,000 separate prompts and manual copying. Claygent can also run on GPT models, Claude models or Clay’s own models such as Neon and Argon.

Isn’t Claygent too unreliable to run on a full lead list?

Claygent is reliable enough for a full lead list once it has passed staged tests on 10, 50 and 100 rows. Most failures we see come from vague prompts or missing inputs, which a strict output format and a required-input filter catch before the run. Claygent Builder lets you test up to 10 inputs at a time without spending credits, so validation costs nothing.