The best enrichment API for AI agents

Ask "what's the best enrichment API for AI agents?" and you'll get the same list of names you'd get for any enrichment tool. That's the wrong list. An agent doesn't shop for data the way a person does — it can't eyeball three sources and pick the one that looks right. It acts on what it's handed, in a single pass, at machine speed. So the real question is narrower and more useful: which enrichment API returns data an autonomous system can safely act on, without a human checking it first?

By that test the field thins out fast. Here is what actually matters, how the main options differ, and where we think abm.dev earns its place — said plainly, including where it isn't the answer.

What "for AI agents" changes

Four things stop mattering, and four start.

• One call, not six. A human can stitch Hunter, Clearbit, Apollo and a scraper together with a spreadsheet and some patience. An agent shouldn't have to. The best API for an agent resolves the sources behind one request.
• A field it can trust. Coverage is easy; trust is hard. If every value comes back with its source and a confidence score, an agent can decide field by field whether to act on it. If it comes back as a bare string, the agent has to believe all of it or none of it.
• A schema that doesn't move. Agents are brittle about shape. One key and one predictable response envelope beats ten endpoints with ten shapes.
• No silent fallbacks. A value that's guessed and returned unmarked is worse than a value that's simply absent. "Cited, or not returned" reads like a limitation until a machine is the one acting on the result — then it's the whole point.

Notice that none of those are about who has the most rows. For an agent, provenance beats volume.

The main options, honestly

abm.dev

The account-based marketing API for AI agents. One key, one schema: search accounts and people, source the buying committee at an account, enrich a person or company into cited multi-source fields, and write the results back to your CRM. Every value returns with its source and a confidence score — cited, or not returned. It speaks MCP natively, so an agent can call it as a tool with no glue code.

Where it's strong: the agent has to trust the data with no human in the loop. Where it isn't the answer: if what you want is the single largest static database to query in bulk, a dataset vendor will out-cover us on raw rows. We resolve fewer values — each one attributable.

Clay

Clay is the best orchestration layer in the category, and it isn't close. It's built for a person assembling a workflow across dozens of providers, with a spreadsheet-shaped canvas and enrichment "waterfalls." That power is exactly the point — and it's overhead for an agent that just needs to call one endpoint and get back a clean, attributed result. If a human is building the sequence, reach for Clay. If an agent is the one calling, an API is the better shape.

People Data Labs

A dataset you query. Enormous coverage, a clean API, and you own the reconciliation — you decide how fresh a record is and which fields to trust, because it's a database read, not a live resolution across sources at call time. The right choice when you want volume and you're happy to do the provenance work yourself.

Explorium, Crustdata, and the rest

Several vendors now market "enrichment for agents," and they're worth a look — Explorium leans MCP-native and broad; Crustdata leans real-time and API-first. Judge them on the same axis as everything above: does a value arrive with its source and a confidence you can act on, or as a bare fact you have to take on faith? That single question sorts the category faster than any feature grid.

How to choose, in one line each

• A human is building the workflow → Clay.
• You want the biggest static database and you'll own provenance → a dataset like People Data Labs.
• An agent calls the endpoint and acts on the result → an API that cites every field. That's the job abm.dev was built for.

Try it

abm.dev's playground is free — one key gets you the REST API or the MCP server at https://mcp.abm.dev/mcp. Point an agent at it and watch what comes back: not a wall of fields, but the ones we can stand behind, each with its source. That's the whole idea.