Which companies are adopting AI agents?

Best move: stop hunting for a master list. Score domains one at a time on what their backend loads and calls.

Why it works: adopting an AI agent means running a model SDK, an orchestration layer, and often a vector store or an MCP endpoint. Those leave signatures. A press release proves none of them shipped.

Key takeaways

• There is no authoritative registry of companies adopting AI agents. You detect adoption per domain by fingerprinting the stack, then aggregate.
• Marketing pages and press releases measure announcements. Backend signals (model SDKs, orchestration, vector stores, MCP endpoints) measure deployment.
• Bassethound’s AI-readiness layer scores each domain on distinct signal groups hit (out of six) and returns a verdict: shipping, experimenting, or none.
• An MCP endpoint or an orchestration framework (LangChain, LlamaIndex, CrewAI) on a domain is a stronger adoption signal than any homepage claim.
• To build a list, you enumerate candidate domains and run sniff_domain on each. The verdict field ranks who ships over who talks.

Why can’t you just pull a list of AI companies?

There is no registry. The lists that exist are built from three weak sources: funding announcements, self-reported firmographic tags (Apollo, ZoomInfo, and Clearbit label “AI” as an industry keyword), and press coverage. Each measures intent or marketing, not deployment. A company can raise an AI round and ship nothing for a year. A quiet team can run agents in production and never blog about it. Directories inherit both errors: false positives from companies that announce, false negatives from companies that ship in silence. The category “adopting AI agents” is also a moving target. Adoption is not binary. It runs from a single chatbot widget to a full agent orchestration backend. A list flattens that gradient into a checkbox. To answer it you need per-domain evidence and a graded verdict, not a name on a roster.

What backend signals prove a company runs AI agents?

Four groups, ordered by strength. Model providers: an Anthropic, OpenAI, Gemini, Cohere, or Mistral SDK or API hostname means the app calls a model. AI SDKs: the Vercel AI SDK, Anthropic SDK, or OpenAI SDK in loaded scripts. Orchestration: LangChain, LlamaIndex, Haystack, or CrewAI signatures mean the app chains steps, which is the line between a chatbot and an agent. Vector stores: Pinecone, Weaviate, Qdrant, Chroma, or Milvus for retrieval. Two signals matter most for agents. An MCP endpoint (a server that exposes tools to models) says the company built for the agent protocol on purpose. An orchestration framework plus a vector store plus a model SDK is the shape of a real agent pipeline. A lone llms.txt or a homepage chatbot is the shallow end. Bassethound classifies llms.txt as standard or technical and separates a widget from a backend, so you do not mistake a support bot for an agent stack.

How do you turn per-domain detection into a company list?

Start with candidate domains, not a search box. Pull them from a vertical, a funding tracker, a conference attendee export, a portfolio page, or your own CRM. Run sniff_domain on each with the standard profile. Read three fields per result: the ai_readiness score (distinct signal groups hit, out of six), the verdict (shipping, experimenting, none), and the icp_signal (hot, warm, cold). Sort by verdict, then by score. Now you have a ranked list where shipping companies sit above experimenting ones, and both sit above the noise. Because the call is keyless and stateless, you can fan it across a few hundred domains without keys, accounts, or stored state between calls. The value is correlation, not just detection: you are not stitching a tech-stack tool to a firmographics tool to an infra tool. One call returns the AI verdict next to the company name, vertical, and contacts, so the list arrives enriched, not as a column of domains you still have to look up.

How do you separate companies shipping agents from ones experimenting?

The verdict is a function of distinct signal groups, not a single detection. One llms.txt file, or one homepage widget, reads as experimenting: a company testing the water. A model SDK plus an orchestration framework plus a vector store reads as shipping: the pieces of a working pipeline are present and wired to the same domain. That correlation across layers is what a single-layer scanner cannot see. A tech-stack tool tells you a site loads the Vercel AI SDK. It cannot tell you the same site also calls Pinecone and exposes an MCP endpoint, which together mean something a lone detection never does. Know the ceiling. A fully server-side backend that proxies every model call can run a serious agent stack and show little to a crawler. Bassethound reports what it could not reach instead of guessing, so a low score is a “not observed here,” not proof of absence. Read the score as strength of evidence, not as a final judgment.

Bassethound perspective

Every firmographics vendor will sell you a filter for “AI companies.” Apollo, ZoomInfo, and Clearbit tag it as an industry keyword, the same way they tag SaaS or fintech. That tag is self-report. It comes from what a company wrote about itself, and it answers a different question than the one you asked. You did not ask who calls themselves an AI company. You asked who is adopting AI agents, which is a claim about the running stack. The only honest way to answer it is to read the stack. Bassethound’s AI-readiness layer is the unclaimed axis here. Everyone else’s AI-readiness check is a homepage scan plus an llms.txt lookup, which any marketing team can pass in an afternoon. We fingerprint the backend: the model SDK, the orchestration layer, the vector store, the MCP endpoint. Those cost real engineering to stand up and there is no reason to fake them. The verdict you get is graded, correlated across five layers, and honest about what the crawl could not see. A list built this way ranks who ships over who announces. That order is the whole point.

Sources

• Model Context Protocol: https://modelcontextprotocol.io
• llms.txt specification: https://llmstxt.org
• Pinecone documentation: https://docs.pinecone.io
• Anthropic documentation: https://docs.anthropic.com