Should an AI agent use Clay people search or Enrich Person?
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Clay AI agent benchmark: people search vs Enrich Person
With Clay, the best way to find who holds a role was free people search followed by one Work Email lookup: Claude Code with Opus 5.5 found 74 to 82 of 100 CMOs for 285 to 293 credits. When the person was already known from a LinkedIn URL, one Enrich Person and Find Contact Details call per profile won instead, with the best runs marking 92 to 96 of 100 people correctly as stayed or left for about 1,400 credits.
By The Prompting Company · Snapshot November 5, 2026
Two Clay tools, two different jobs
Clay gives an AI agent two ways to work with people data. Find People (people search on the Public API) returns names and titles at a company, and sourcing a list this way uses no Data Credits. Enrich Person and Find Contact Details is a saved Clay function that returns a full profile and contact details for one person, and it costs credits when it returns data. Clay does not charge Data Credits or Actions when an enrichment returns nothing (Clay pricing).
Two tasks in our benchmark of seven AI coding agents tested both: finding an unknown person (the CMO at 100 public companies) and checking a known person's current job (100 CFOs with LinkedIn URLs). The right tool was different each time.
When the person is unknown, search first
In the CMO task, the agent starts with a company name and has to find who holds the title. Opus used the same workflow in all three runs: Find People for each company, keep the person with the Chief Marketing Officer title, then run the Work Email function for that one person. Its Clay function log shows nothing but Work Email calls, 79 to 84 per run, so every credit went to an email for a person it had already chosen.
| Approach | Run | Clay credits | CMOs found (of 100) |
|---|---|---|---|
| People search, then Work Email for the chosen person | Opus, runs 1 to 3 | 285 to 293 | 74 to 82 |
| Claude Code with Sonnet 5, run 2 | 302 | 79 | |
| Enrich Person and Find Contact Details on candidates | Sonnet, run 3 | 2,780 | 78 |
| Factory with Kimi K3, runs 2 and 3 | 1,349 to 1,360 | 71 to 73 |
Sonnet is the cleanest comparison: the same agent scored 79 for 302 credits with people search and 78 for 2,780 credits when it enriched 163 candidates. Find People costs 0 Data Credits, so choosing first keeps the cost under 3 credits per company.
When the person is known, enrich once
In the job change task, each row already names the person and links their LinkedIn profile. Here, one enrichment per profile, at about 14 credits per person, decided the most rows.
| Approach | Runs | Clay credits | Correct (of 100) |
|---|---|---|---|
| Enrich Person and Find Contact Details for every profile | Sonnet, OpenCode Kimi, Factory Kimi, Grok, Pi, Opus runs 2 and 3 (17 runs) | 1,404 to 1,635 | 73 to 96 |
| Clay search only, no paid enrichment | Codex with GPT-6 Sol, runs 1 to 3 | 0 | 68 to 78 |
Thirteen of those 17 enrichment runs scored 92 or more, and Opus's two runs scored 94 both times. The top runs compared each current company with the one on file and wrote a decision for nearly every row.
The rule: search to choose who, enrich once you know who
| Your starting point | Use in Clay | Why |
|---|---|---|
| A company and a role | Find People, then Work Email for the one match | Search is free, and you pay only for the person you keep |
| A known person with a LinkedIn URL | Enrich Person and Find Contact Details, once per person | One call returns the current role, and nearly every row gets decided |
| A known person, a handful of checks | Find People is fine | Speed matters less on a short list |
Put simply: free search to decide who, paid enrichment once you know who. For the full CMO workflow see How can an AI agent find a company's CMO with Clay, and what does it cost?, and for job changes see How do I check 100 contacts for job changes with Clay?
How we tested
Seven AI coding agents ran each task 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 run that hit the limit counts as 0; platform failures were rerun. Clay credits are measured as the change in the Clay workspace credit balance. A model judge read every log to confirm that all values came from Clay. Tasks, prompts 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 say which case you are in: "Use Find People to pick the person before running any paid lookup", or "Run Enrich Person and Find Contact Details once per LinkedIn URL and decide every row."
Common questions
Should an AI agent use Clay people search or Enrich Person? Use people search when you need to find who holds a role, and Enrich Person when you already know the person: search found 74 to 82 CMOs for under 3 credits per company, and enrichment, at about 14 credits per person, marked up to 96 of 100 people correctly as stayed or left.
Is Clay people search free? Sourcing people with Find People uses no Data Credits. In our CMO runs, Opus's only paid calls were Work Email lookups.
Should an agent enrich every candidate to be safe? Search first is the better path. With search first, Sonnet found 79 CMOs for 302 credits, about 3 per company; enriching every candidate, it found 78 for 2,780.
Can free search replace enrichment for job changes? For a full list, one enrichment per profile, at about 14 credits per person, decided the most rows and scored up to 96. Search-only runs spent 0 credits and scored 68 to 78.