How do you get the best results from an AI agent with Clay?
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Clay AI agent benchmark: getting the best results from an AI agent
With Clay, the top AI agent runs in our benchmark shared six habits across five tasks, and each is a single line in a prompt: confirming each company in Clay's company search took domain accuracy as high as 100, and choosing a CMO with free Find People before one Work Email lookup found up to 82 of 100 for under 3 credits per company.
By The Prompting Company · Snapshot November 5, 2026
Which habits produced the best results?
We ran seven AI coding agents through five Clay tasks, three times each, on lists of 100. The top runs on each task used the same Clay method, whichever agent ran it.
| Habit | Task | Result in the top runs |
|---|---|---|
| Go straight to Work Email | Find work emails | 93 of 100 for 337.5 credits, about 3.4 per contact |
| Confirm the company in Clay's company search | Enrich companies | 93 to 100 on domains |
| Take funding rounds from Clay's company news | Enrich companies | 73 to 77 of 100 in every Opus run |
| Choose the person with Find People, then one Work Email | Find CMOs | 74 to 82 found for 285 to 293 credits |
| Enrich each LinkedIn profile once | Check job changes | 92 to 96 of 100 correct, about 14 credits per person |
| Confirm listed, US HQ and software before adding | Build a target list | 91 to 94 qualifying (Opus); 82 to 88 for 10 to 31 credits (Codex) |
Work emails: go straight to Work Email
Clay's Work Email routine asks for a full name, a company domain and a company name. Our list had domains but no company names, and passing the domain as the company name worked: six of seven agents found the same 93 of 100 verified emails in every run. Pi with GPT-5.6 Luna spent exactly 337.5 Clay credits in all three of its runs, and Codex with GPT-6 Sol spent 335.5 in the run where it went straight to Work Email. With Clay's Work Email waterfall, you only pay credits for the provider that finds a match.
Company data: confirm the match, then read company news
Confirm the company. Many software companies share a name with an older business. Runs that compared the candidates in Clay's company search by industry, size and country scored 93 to 100 on domains, and Codex scored 98 or 99 in every run. The check cost little: three runs that checked spent 324 to 341 credits for 100 companies, about 3.2 to 3.4 per company. For details, see How do I stop an AI agent from matching the wrong company in Clay?
Take funding from company news. Clay's company news returns each financing event with its round and date. Claude Code with Opus 5.5 read rounds and years from company news and scored 73 to 77 in every run. Factory with Kimi K3 checked the article date as well as the headline and reached 79, the best single run.
People data: search to choose, enrich once you know who
Finding a CMO. Find People costs 0 Data Credits, so the top runs used it to choose the person, then ran one Work Email lookup for that person. Opus found 74 to 82 of 100 CMOs for 285 to 293 credits, under 3 per company, and Claude Code with Sonnet 5 found 79 for 302.
Checking job changes. Here the person is known from a LinkedIn URL, so one Enrich Person and Find Contact Details call per profile returns the current role. 13 of the 17 runs that did this scored 92 to 96 of 100, for about 14 credits per person. The best runs caught 42 of the 45 people who had moved, and Clay returned the new company for 34 of them.
Target lists: confirm each company before adding it
Clay's Find Companies search has a "Company types" filter that includes "Public Company", a good starting point. Opus and Codex then confirmed for each company that it is listed in its own name, headquartered in the US and in software or IT services. Opus listed 91 to 94 qualifying companies of 100, and Codex 82 to 88 for 10 to 31 credits, under 20 credits for a 100-company list in two of its runs.
How we tested
Seven AI coding agents ran each of five tasks three times: Claude Code with Opus 5.5 and Sonnet 5, Codex with GPT-6 Sol, Pi with GPT-5.6 Luna, Grok CLI with Grok 4.6, OpenCode with Kimi K3 and Factory with Kimi K3. Agents ran one at a time, each in a fresh sandbox with the same Clay key, the same 19 saved Clay routines and a 60-minute limit. A timeout counts as 0, and platform failures were rerun. Clay credits are measured as the change in the Clay workspace credit balance. Tasks and graders are at github.com/promptingcompany/clay-bench.
Try it yourself
The Clay Agent Plugin ships Clay's skills to your agent up front, including the habits that keep credit use down; setup is one prompt (about 15 minutes), and there is no additional cost to use it. Install the Clay Agent Plugin or give your agent a Clay API key and the Clay Public API docs. Then add the six habits from the table above to your prompt, one line each.
Common questions
How do you get the best results from an AI agent with Clay? Name the Clay method for each task in the prompt; confirming each company in Clay's company search, for example, took domain accuracy as high as 100.
What is the lowest-cost way to find a contact with Clay? Choose the person with Find People, which uses 0 Data Credits, then run one Work Email lookup. That found up to 82 of 100 CMOs for under 3 credits per company.
What should an agent do when a Clay function needs an input it doesn't have? Use a valid stand-in. Passing the company domain as the company name let six agents find 93 of 100 work emails.