Connect People Data Labs to AI Agents: Automate Talent Identification

You want to connect People Data Labs to an AI agent so your system can autonomously source candidates, enrich inbound leads, clean messy job titles, and route prospects based on firmographics. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to write and maintain complex search queries and pagination logic by hand.

Giving a Large Language Model (LLM) read and write access to a massive data warehouse like People Data Labs is an engineering challenge. You either spend weeks building a custom API integration that maps their search syntax to a format the LLM understands, or you use a managed infrastructure layer that provides agent-ready tools out of the box. If your team uses ChatGPT, check out our guide on connecting People Data Labs to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting People Data Labs to Claude. For developers building custom autonomous workflows across any framework, you need a programmatic way to fetch these tools and bind them directly to your agent.

This guide breaks down exactly how to fetch AI-ready tools for People Data Labs, bind them natively to an LLM using frameworks like LangChain, LangGraph, CrewAI, or the Vercel AI SDK, and execute multi-step talent identification workflows. For a deeper look at the architecture behind this approach, refer to our research on architecting AI agents and the SaaS integration bottleneck.

Why a Unified Tool Layer Matters for Data APIs

Before writing integration code, you must decide what layer your agent talks to. When dealing with enrichment and data intelligence APIs, direct endpoint access pushes vendor-specific quirks directly into your LLM's context window.

People Data Labs is essentially a vast Elasticsearch cluster wrapped in an API. The model has to know when to use an exact match query versus a fuzzy search, how to structure nested JSON filters, and how to handle specific HTTP error codes that actually mean "no data found" rather than "server error." Every quirk is a hallucination risk.

Truto collapses this complexity by mapping the underlying API into standard proxy tools - Resources and Methods defined in a clean JSON schema. Your agent sees simple, descriptive functions like people_data_labs_people_search and people_data_labs_companies_enrich with strict JSON schemas determining the inputs. Invalid arguments are rejected before they hit the network, and the LLM only chooses from a stable list of operations.

The Engineering Reality of the People Data Labs API

Building an AI agent against the People Data Labs API introduces specific integration challenges that break standard REST assumptions. If you hardcode these interactions into your agent's prompt, you will spend your sprints writing defensive code instead of improving the model's reasoning.

The Elasticsearch vs SQL Query Trap

The primary search endpoints for People Data Labs require the client to submit either an Elasticsearch query (the query parameter) or a SQL-like string (the sql parameter). You must pass exactly one. If an LLM tries to pass both, or if it invents an unsupported SQL operator, the API rejects the request.

Training an LLM to reliably write raw Elasticsearch queries on the fly is highly error-prone. A strict tool schema forces the LLM to provide exact fields and prevents it from mixing query languages, isolating the complex search syntax behind a deterministic validation layer.

Scroll Token Pagination

People Data Labs does not use standard limit and offset pagination. When an agent searches for records, the API returns a page of results alongside a scroll_token. To get the next page, the exact token must be passed in the subsequent request. A null token indicates the end of the dataset. Agents are notoriously bad at remembering opaque string tokens across multiple turns. Your tool layer must strictly enforce the passing of the scroll_token without the LLM trying to increment it like a standard page number.

404 No-Match Semantics

In typical REST APIs, a 404 means the endpoint doesn't exist. In People Data Labs, a 404 on an enrichment or identification endpoint simply means "we have no data matching this person or company." An un-tuned agent will often interpret a 404 as a catastrophic failure and abort the workflow. The tool integration must gracefully catch the 404 and return it to the agent as { "status": 404, "data": [] }, signaling an empty result set rather than a system crash.

Strict Rate Limiting and Backoff

People Data Labs enforces strict rate limits depending on the endpoint (e.g., 10 calls per minute for Identification). When these limits are hit, the upstream API returns an HTTP 429 Too Many Requests.

Truto does not retry, throttle, or apply backoff on rate limit errors. Instead, Truto passes that 429 error directly back to the caller, normalizing the upstream rate limit information into standardized IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset). The caller - your agent framework - is entirely responsible for reading these headers and executing a retry/backoff strategy. Do not expect the infrastructure to absorb rate limit errors for you.

sequenceDiagram
    participant Agent as AI Agent
    participant Truto as Truto Proxy Layer
    participant Upstream as Upstream API (PDL)
    Agent->>Truto: Call people_data_labs_people_identify
    Truto->>Upstream: Forward API Request
    Upstream-->>Truto: 429 Too Many Requests
    Truto-->>Agent: Pass 429 Error with ratelimit-* Headers
    Note over Agent: Agent reads ratelimit-reset<br>and pauses execution
    Agent->>Agent: Wait for reset window
    Agent->>Truto: Retry people_data_labs_people_identify
    Truto->>Upstream: Forward API Request
    Upstream-->>Truto: 200 OK + Match Data
    Truto-->>Agent: Return Tool Result

Available Tools for People Data Labs

By fetching tools via Truto's /integrated-account/:id/tools endpoint, you instantly provide your agent with native access to People Data Labs. Here are the highest-leverage hero tools for automating talent and firmographic intelligence.

people_data_labs_people_search

Search the People Data Labs person dataset using an Elasticsearch query or SQL filter. This tool returns up to 100 records per page and provides a scroll_token for pagination. A no-match query returns an empty result set.

"Find software engineers in San Francisco who previously worked at Stripe or Plaid. Page through the results until you have 50 candidates, and format their LinkedIn URLs and current company names into a JSON array."

people_data_labs_people_enrich

Enrich a single person's profile using specific locators. This requires at least a profile URL, email, phone, or a combination of name and location/company. It returns a highly detailed profile including work history, education, and contact data.

"Take this list of inbound webinar attendees with just names and personal emails. Enrich each person using People Data Labs to find their current employer, job title, and LinkedIn profile."

people_data_labs_people_identify

Identify a person when you have fragmented or incomplete data. Unlike enrichment, which seeks a 1-to-1 match, identify returns up to 20 candidate profiles sorted by a match score. This is critical for resolving messy CRM records.

"I have a lead named 'J. Smith' at a company called 'Acme Corp' located in New York. Use the identification tool to find the most likely match and return the candidate with the highest match score."

people_data_labs_companies_search

Search for companies using Elasticsearch or SQL filters based on industry, size, funding, or location. Useful for building target account lists before executing person-level searches.

"Find B2B SaaS companies founded after 2020 with between 50 and 200 employees located in London. Return the list of company IDs and domains."

people_data_labs_companies_enrich

Enrich a single company record using a domain, ticker, or name. Returns standardized firmographic data including exact industry categorization, headcount, and social profiles.

"We just received an inbound signup from an email ending in @vercel.com. Enrich the Vercel company record to determine their exact employee count and industry. If they are over 100 employees, flag them as an enterprise lead."

people_data_labs_job_titles_enrich

Clean and standardize raw, messy job titles. This tool parses custom titles and returns standard categorizations (cleaned_job_title), similar titles, and relevant skills associated with the role.

"Clean the job title 'Chief Happiness Officer & Lead Code Wizard'. Extract the standardized job title and list the relevant skills associated with this role so we can route the lead to the correct sales territory."

For the complete inventory of available endpoints and their strict JSON schemas, view the People Data Labs integration page.

Workflows in Action

AI agents excel at multi-step reasoning. By chaining these tools together, you can automate complex data operations that typically require a dedicated revenue operations engineer.

1. Automated Executive Sourcing

Recruiting teams spend hours building boolean strings to find niche candidates. An AI agent can handle the entire pipeline autonomously.

"Build a list of potential VP of Engineering candidates. First, search for enterprise software companies in Austin, Texas with over 500 employees. Extract their company IDs. Then, search for people currently holding 'VP of Engineering' or 'Director of Engineering' titles at those specific company IDs. Return a final list of 20 enriched profiles including their email addresses and LinkedIn URLs."

Step-by-step execution:

1. Agent calls people_data_labs_companies_search using a SQL filter for location and size.
2. Agent parses the returned company array and extracts the unique company identifiers.
3. Agent constructs a new query and calls people_data_labs_people_search filtering by the extracted company IDs and target job titles.
4. Agent evaluates the pagination scroll_token and calls the search tool again if fewer than 20 candidates were returned on the first page.
5. Agent outputs the final consolidated JSON list.

2. Inbound Lead Enrichment and Routing

Marketing forms often capture minimal data to increase conversion rates. Agents can fill in the gaps in real-time.

"A new lead just submitted a form with the name 'Alex Chen' and the email 'alex@tryretool.com'. Identify this person to find their full profile. Next, enrich the company domain 'tryretool.com'. Finally, clean Alex's job title. Based on the company size and Alex's seniority, decide if this lead goes to the Enterprise or Mid-Market sales queue."

Step-by-step execution:

1. Agent calls people_data_labs_people_identify using the email address.
2. Agent receives a match, extracting the person's raw job title and full name.
3. Agent calls people_data_labs_companies_enrich passing the domain to fetch employee headcount.
4. Agent calls people_data_labs_job_titles_enrich to normalize the raw title into a standard seniority level.
5. Agent evaluates the firmographic data against its routing prompt instructions and outputs the final decision.

Building Multi-Step Workflows

To build these workflows in code, you must initialize the Truto SDK, fetch the People Data Labs tools, and bind them to your agent. Because Truto normalizes the API surface, this code works agnostically across standard agent frameworks.

Crucially, your execution loop must handle HTTP 429 rate limits. When your agent hits a limit, Truto will pass the 429 error and the ratelimit-reset header back. The agent loop must catch this, wait for the specified duration, and instruct the LLM to retry the tool call.

Here is a production-grade TypeScript example using LangChain.js.

import { ChatOpenAI } from "@langchain/openai";
import { TrutoToolManager } from "@trutohq/truto-langchainjs-toolset";
import { HumanMessage } from "@langchain/core/messages";
 
async function runDataEnrichmentAgent() {
  // Initialize the LLM
  const llm = new ChatOpenAI({
    modelName: "gpt-4o",
    temperature: 0,
  });
 
  // Initialize Truto Tool Manager with your PDL integrated account ID
  const toolManager = new TrutoToolManager({
    trutoToken: process.env.TRUTO_API_KEY,
    accountId: process.env.PDL_ACCOUNT_ID,
  });
 
  // Fetch People Data Labs tools via the /tools API
  const tools = await toolManager.getTools();
 
  // Bind the tools to the LLM
  const agentWithTools = llm.bindTools(tools);
 
  let messages = [new HumanMessage("Enrich the company domain 'openai.com' and tell me their exact headcount.")];
 
  // Agent Execution Loop with Rate Limit Handling
  while (true) {
    const response = await agentWithTools.invoke(messages);
    messages.push(response);
 
    // If the LLM didn't call a tool, we are done
    if (!response.tool_calls || response.tool_calls.length === 0) {
      console.log("Final Result:", response.content);
      break;
    }
 
    // Execute requested tool calls
    for (const toolCall of response.tool_calls) {
      const selectedTool = tools.find((t) => t.name === toolCall.name);
      
      if (selectedTool) {
        try {
          const toolResult = await selectedTool.invoke(toolCall.args);
          messages.push({
            role: "tool",
            tool_call_id: toolCall.id,
            content: toolResult,
          });
        } catch (error: any) {
          // Handle 429 Rate Limits exposed by Truto
          if (error.status === 429) {
            const resetTime = error.headers['ratelimit-reset'];
            console.warn(`Rate limit hit. Waiting until ${resetTime} before retrying...`);
            
            // Implement your backoff wait logic here based on the header
            // await waitForReset(resetTime);
            
            // Inform the LLM that the tool failed due to rate limits and it should try again
            messages.push({
              role: "tool",
              tool_call_id: toolCall.id,
              content: JSON.stringify({ 
                error: "429 Too Many Requests. Rate limit hit. Please retry the exact same tool call now that the backoff is complete." 
              }),
            });
          } else {
             // Handle 404 No Match or other errors
             messages.push({
              role: "tool",
              tool_call_id: toolCall.id,
              content: JSON.stringify({ error: error.message }),
            });
          }
        }
      }
    }
  }
}
 
runDataEnrichmentAgent();

Moving Beyond Hardcoded Scripts

Integrating People Data Labs manually requires you to maintain Elasticsearch query parsers, manage scrolling pagination tokens, and write custom logic to catch 404s when profiles aren't found. By using an infrastructure layer that maps the API into standard proxy tools, you remove the integration maintenance burden from your engineering team.

Your AI agents get deterministic, validated functions to call. Your LLM isn't burning context tokens trying to figure out how to format nested JSON queries. The result is an autonomous system that can identify, enrich, and clean global talent data reliably in production.