Can AI Agents Handle Domain Due Diligence Autonomously?

#aiagents #domainduediligence #riskassessment #rapidapi #mcp #compliance #api

The latest wave of autonomous AI agents — like the Talivia Group agent project, which installs and verifies website analytics through MCP — is a clear signal: agents are no longer just chatbots. They are becoming operators that can inspect, validate, and act on real-world web properties.

But before an AI agent signs off on a partnership, processes a transaction, or ingests a third-party data feed, it should answer one hard question: can I trust this domain?

That is where domain due diligence comes in. Traditionally, it means stitching together WHOIS records, IP geolocation, company registries, email reputation, and sanctions lists — a slow, manual process. The Portfolio Investigate API collapses that workflow into a single call and gives the agent a plain-English verdict plus a natural-language follow-up endpoint.

What makes a domain “trustworthy”?

For an AI agent, trust is not a feeling; it is a dossier. A reliable domain investigation should include:

• Identity: who registered the domain, when, and from where
• Location: where the server is physically hosted
• Entity: what company is behind the site
• Reputation: whether the contact email or domain appears on risky lists
• Compliance: whether any related party appears on sanctions lists

The Portfolio Investigate API aggregates five portfolio APIs — WHOIS, IP Geo, Company, Email, and Sanctions — and runs cross-product inference. Instead of returning five separate JSON blobs, it produces one unified report with a risk score and a human-readable verdict. It also exposes a POST /ask endpoint so an agent (or a compliance officer) can ask follow-up questions in plain English.

One-call domain dossier

Imagine your agent is evaluating example.com before onboarding it as a partner. A single GET request returns a structured dossier.

cURL

curl --request GET \
  --url 'https://portfolio-investigate.p.rapidapi.com/v1/investigate?domain=example.com' \
  --header 'X-RapidAPI-Key: YOUR_RAPIDAPI_KEY' \
  --header 'X-RapidAPI-Host: portfolio-investigate.p.rapidapi.com'

Python

import requests

def investigate_domain(domain: str, api_key: str):
    url = "https://portfolio-investigate.p.rapidapi.com/v1/investigate"
    headers = {
        "X-RapidAPI-Key": api_key,
        "X-RapidAPI-Host": "portfolio-investigate.p.rapidapi.com",
    }
    params = {"domain": domain}
    response = requests.get(url, headers=headers, params=params, timeout=30)
    response.raise_for_status()
    return response.json()

report = investigate_domain("example.com", "YOUR_RAPIDAPI_KEY")
print(report["verdict"])
print(report["risk_score"])

A typical response might look like this:

{
  "domain": "example.com",
  "risk_score": 23,
  "verdict": "Low risk. Registrant organization matches public company record. Server hosted in US. No sanctions hits on associated names or emails.",
  "whois": {
    "registrar": "Example Registrar, Inc.",
    "created": "2019-04-12",
    "registrant_org": "Example LLC"
  },
  "ip_geo": {
    "country": "US",
    "city": "Ashburn"
  },
  "company": {
    "name": "Example LLC",
    "status": "active"
  },
  "email": {
    "disposable": false,
    "deliverable": true
  },
  "sanctions": {
    "hits": []
  }
}

Because the API already does the cross-product inference, your agent does not have to write brittle rules like “if WHOIS country != IP country then reject.” It can rely on the aggregated risk_score and verdict, then layer its own business logic on top.

Natural-language follow-up with POST /ask

Sometimes a score is not enough. A compliance officer — or an LLM-powered agent — needs to ask context-specific questions. The POST /ask endpoint turns the dossier into a conversational interface.

cURL

curl --request POST \
  --url https://portfolio-investigate.p.rapidapi.com/v1/ask \
  --header 'X-RapidAPI-Key: YOUR_RAPIDAPI_KEY' \
  --header 'X-RapidAPI-Host: portfolio-investigate.p.rapidapi.com' \
  --header 'Content-Type: application/json' \
  --data '{
    "domain": "example.com",
    "question": "Is the registrant organization based in a sanctioned country or linked to any sanctioned entity?"
  }'

Python

def ask_about_domain(domain: str, question: str, api_key: str):
    url = "https://portfolio-investigate.p.rapidapi.com/v1/ask"
    headers = {
        "X-RapidAPI-Key": api_key,
        "X-RapidAPI-Host": "portfolio-investigate.p.rapidapi.com",
        "Content-Type": "application/json",
    }
    payload = {"domain": domain, "question": question}
    response = requests.post(url, headers=headers, json=payload, timeout=30)
    response.raise_for_status()
    return response.json()

answer = ask_about_domain(
    "example.com",
    "Does this domain show any signs of impersonating a financial institution?",
    "YOUR_RAPIDAPI_KEY",
)
print(answer["response"])

This is especially useful in agent loops. The agent can call investigate_domain once, store the dossier in memory, then use ask iteratively to resolve ambiguities before making a final decision.

Wiring it into an autonomous agent

Here is a minimal agent-style decision function that consumes the API. It fetches the dossier, checks the risk score, and falls back to the LLM Ask endpoint when the score is borderline.

def agent_domain_decision(domain: str, api_key: str, risk_threshold: int = 60):
    report = investigate_domain(domain, api_key)
    score = report.get("risk_score", 100)

    if score < risk_threshold // 2:
        return {"decision": "approve", "reason": report["verdict"]}

    if score >= risk_threshold:
        return {"decision": "reject", "reason": report["verdict"]}

    # Borderline: ask a targeted question
    follow_up = ask_about_domain(
        domain,
        "Summarize the top compliance concerns for this domain in one sentence.",
        api_key,
    )
    return {
        "decision": "manual_review",
        "reason": follow_up.get("response", "No additional context available."),
        "report": report,
    }

You can expose this function as an MCP tool, a LangChain tool, or a simple FastAPI endpoint. The key point is that the heavy lifting — data aggregation, inference, and natural-language reasoning — is outsourced to the Portfolio Investigate API.

How to use Portfolio Investigate API

1. Get a RapidAPI key and subscribe to the API: Portfolio Investigate API on RapidAPI.
2. Test the one-call report with the cURL or Python snippet above, replacing YOUR_RAPIDAPI_KEY with your key.
3. Experiment with POST /ask to ask domain-specific questions in natural language.
4. Integrate the verdict into your agent loop, dashboard, or compliance workflow.

For more examples and the underlying project structure, check out the GitHub repository:

https://github.com/On13uka/portfolio-api

Conclusion

Autonomous AI agents are only as good as the data they can trust. Domain due diligence is too important — and too fragmented — to leave to manual checks. The Portfolio Investigate API gives agents (and the humans supervising them) a single source of truth: a unified dossier, a plain-English verdict, and an LLM-powered Ask endpoint for follow-up reasoning.

If you are building agentic workflows around website verification, transaction risk, or compliance screening, this API is a practical place to start.