Connect Captain Data to AI Agents: Map Work History & Professional Data

You want to connect Captain Data to an AI agent so your system can autonomously map work histories, enrich professional data, and execute complex lead sourcing workflows based on real-time intelligence. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to build and maintain a custom integration for a highly complex scraping and enrichment API.

Giving a Large Language Model (LLM) read and write access to your Captain Data instance is a significant engineering challenge. You either spend sprints writing defensive code to handle credit quotas, pagination cursors, and rate limits, or you use a managed infrastructure layer that handles the boilerplate tool-calling schemas for you. If your team uses ChatGPT, check out our guide on connecting Captain Data to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Captain Data to Claude. For developers building custom autonomous workflows, you need a programmatic way to fetch these tools and bind them to your agent framework.

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

The Engineering Reality of the Captain Data API

Giving an LLM access to external data sounds simple during local prototyping. You write a standard fetch wrapper and decorate it as a tool. In production, against a specialized system like Captain Data, this naive approach collapses.

Captain Data is fundamentally an automation and data extraction engine that interacts with platforms like LinkedIn Sales Navigator. Its API does not behave like a standard CRUD database. If you hardcode these interactions into your agent, you will spend your time debugging failed tool calls instead of improving your agent's reasoning capabilities.

Credit-Aware Execution and Quotable Cost Structures

Unlike standard SaaS APIs that allow unlimited reads within a rate limit, Captain Data operates on a strict credit economy. Actions cost credits, and often, the cost is dynamic. For example, executing a search might cost 1 credit per request plus 1 credit per result returned.

If you hand raw API access to an LLM and tell it to "find all software engineers in San Francisco," the agent will loop through hundreds of pagination cursors, draining your entire monthly workspace quota in minutes. A unified tool layer limits the agent's surface area, explicitly defining required parameters and bounds so the LLM cannot hallucinate infinite loops.

Deeply Nested Data Models

Enrichment data is inherently unstructured and deeply nested. When an agent requests a person's profile, Captain Data returns a massive JSON payload containing arrays of objects for experiences, skills, education, and certifications. Standard LLMs struggle when forced to parse and reason over a 15,000-line JSON payload injected directly into their context window. By mapping specific resources like /people/experiences or /people/skills into atomic tools, the agent can fetch exactly the slice of data it needs, preserving context limits and reducing token costs.

Strict Rate Limiting and Required Backoff Mechanics

Captain Data enforces aggressive rate limits depending on your plan tier (often 5 to 100 requests per minute). When building AI agents, handling these limits incorrectly leads to catastrophic workflow failures.

Factual note on how Truto handles rate limits: Truto does not automatically retry, throttle, or apply backoff on rate limit errors. If an LLM is waiting on a network request, silently absorbing a 60-second backoff at the proxy layer would cause the agent framework's connection to timeout, or worse, trick the agent into retrying the tool call and burning duplicate credits.

Instead, when Captain Data returns an HTTP 429, Truto passes that error directly back to the caller. Crucially, Truto normalizes the upstream rate limit information into standardized IETF headers: ratelimit-limit, ratelimit-remaining, and ratelimit-reset. Your agent framework is responsible for catching this 429 error, reading the ratelimit-reset integer, sleeping the execution thread, and resuming the workflow safely.

AI-Ready Hero Tools for Captain Data

Instead of exposing the raw endpoints to your agent, Truto maps Captain Data's endpoints into specific, strictly-typed functional tools. Your agent sees concrete action names and JSON schemas, eliminating the need to guess URL structures or query parameter formatting.

Here are the highest-leverage hero tools available for the Captain Data integration.

1. list_all_captain_data_search_companies

This tool allows the agent to search for companies using a structured LinkedIn Sales Navigator query. It returns a paginated list of companies containing crucial firmographic data like UID, name, description, size, and social URLs.

Contextual usage notes: Agents should use this as the starting point for account-based workflows. The query parameter must be strictly formatted. The LLM must be prompted to handle the 25-item per page limitation effectively.

"I need to map out the mid-market software landscape. Use the company search tool to find B2B SaaS companies in London with a headcount between 50 and 200 employees."

2. list_all_captain_data_companies_enriches

Once an agent identifies a target company, it uses this tool to execute a deep enrichment operation based on the company's LinkedIn URL. This pulls in comprehensive headquarters data, total funding, affiliated entities, and precise industry categorization.

Contextual usage notes: The only required input is li_company_url. Agents will typically chain this immediately after a successful list_all_captain_data_search_companies call to get the necessary depth before drafting account plans.

"Take the top 5 companies we just found from the London SaaS search, run a full enrichment on each of their company URLs, and summarize their recent funding and exact headquarters locations."

3. list_all_captain_data_search_peoples

This tool executes a targeted search for individuals based on a Sales Navigator query. It returns 25 people per page, including their current job titles, company associations, and direct LinkedIn profile URLs.

Contextual usage notes: This tool is computationally expensive (1 credit per request + 1 per result). Agents should be explicitly prompted with a system instruction to construct highly specific search queries to minimize result bloat and preserve workspace credits.

"Find the current Vice Presidents of Engineering at the 5 enriched SaaS companies we identified. Return their names, current titles, and profile URLs."

4. list_all_captain_data_people_enriches

Given a LinkedIn profile URL, this tool returns a full professional identity payload. By setting full_enrich=true, the agent can pull headline, current location, active job role, and a preview of the individual's skills.

Contextual usage notes: This provides the core context needed for personalized outreach. It is required to pass li_profile_url.

"Run a full profile enrichment on the VP of Engineering at Acme Corp using their profile URL. Ensure you set full_enrich to true so we can see their headline and current location."

5. list_all_captain_data_people_experiences

This is a highly specialized tool that extracts the complete, chronological work history of a target individual. It returns one discrete record per past experience, including job title, company name, tenure dates, and location.

Contextual usage notes: Providing the people_uid (retrieved from previous searches) is preferred over the URL for faster resolution. Agents use this to look for specific career patterns (e.g., "Has this person worked at a competitor?").

"Extract the full work history for the VP of Engineering using their UID. Tell me if they have ever held a role with 'Security' or 'Infrastructure' in the title during the past ten years."

6. list_all_captain_data_workspaces_consumptions

This administrative tool returns the current billing month's credit consumption, providing a granular, per-action breakdown of credits used.

Contextual usage notes: This is an essential safety guardrail. Mature agent workflows should invoke this tool before executing large loops of searches to ensure there is sufficient quota remaining to complete the task.

"Before we proceed with the batch enrichment of 500 contacts, check our workspace consumption. How many credits do we have left for this billing period, and is it safe to proceed?"

To view the complete inventory of available tools, required arguments, and JSON schemas for this integration, visit the Captain Data integration page.

Workflows in Action

When you bind these normalized tools to an LLM, you unlock autonomous workflows that previously required complex, brittle orchestration code. Here are two concrete examples of how an agent uses the Captain Data toolset in the real world.

1. Autonomous B2B Account Mapping

Sales operations teams spend hours manually mapping accounts and identifying decision-makers. An AI agent can compress this into seconds.

"Map out the engineering leadership for Acme Corp. Find the company, enrich its profile to confirm it's the SaaS company in London, find anyone with a 'VP of Engineering' or 'CTO' title there, and give me a summary of their past three job roles."

Execution Steps:

1. The agent calls list_all_captain_data_search_companies with the query "Acme Corp".
2. It extracts the li_company_url from the results and calls list_all_captain_data_companies_enriches to verify the firmographics.
3. The agent calls list_all_captain_data_search_peoples using a precise query combining the company name and targeted job titles.
4. For the matching individuals, it extracts their people_uid and calls list_all_captain_data_people_experiences.
5. The agent synthesizes the results, summarizing the work history of the key technical leaders and returning a formatted briefing document.

2. Proactive Credit-Aware Lead Sourcing

Because Captain Data operates on a strict credit system, agents must be explicitly instructed to manage operational costs.

"I need to source 100 new marketing leads. But first, check our workspace consumption. If we have used more than 80% of our quota this month, stop and warn me. If we are under 80%, proceed to search for 'CMO' titles at fintech companies and run a full enrichment on the first 10 results."

Execution Steps:

1. The agent calls list_all_captain_data_workspaces_consumptions to read the current billing state.
2. The LLM evaluates the JSON response. If the used credits exceed the prompt's threshold, it halts execution and returns an alert to the user.
3. If the quota is safe, it calls list_all_captain_data_search_peoples with the specified industry and title query.
4. It loops through the first 10 li_profile_url results and sequentially calls list_all_captain_data_people_enriches.
5. The user receives 10 fully enriched profiles, safely executed without blowing through the month's budget.

Building Multi-Step Workflows

To build these agents, you need to connect the Truto tool registry to your framework of choice. Truto exposes proxy endpoints via the /tools API. The Truto SDKs handle the dynamic fetching of these JSON schemas and register them directly with the LLM.

The Architecture of the Proxy Layer

Directly integrating means maintaining boilerplate. The Truto proxy architecture sits between your agent and Captain Data. It manages the OAuth state and exposes standard REST CRUD methods derived from the underlying resources.

flowchart TD
    A["Agent Framework<br>(LangChain, CrewAI)"] -->|"execute tool"| B["Truto Tool Manager<br>(Unified Layer)"]
    B -->|"Normalized proxy request"| C["Captain Data API"]
    C -->|"Raw nested JSON"| B
    B -->|"Validated JSON response"| A

Implementing the Agent Loop (with Rate Limit Handling)

Here is how to build a resilient agent loop in TypeScript using the truto-langchainjs-toolset. This implementation explicitly demonstrates how to handle the HTTP 429 rate limit errors that Captain Data will throw during heavy concurrent execution.

import { ChatOpenAI } from "@langchain/openai";
import { TrutoToolManager } from "@trutohq/truto-langchainjs-toolset";
import { HumanMessage } from "@langchain/core/messages";
 
// 1. Initialize the LLM
const llm = new ChatOpenAI({
  modelName: "gpt-4o",
  temperature: 0,
});
 
// 2. Initialize the Truto Tool Manager with your Integrated Account ID
const toolManager = new TrutoToolManager({
  integratedAccountId: process.env.CAPTAIN_DATA_INTEGRATED_ACCOUNT_ID,
  trutoApiKey: process.env.TRUTO_API_KEY,
});
 
async function runAutonomousSourcing() {
  // 3. Fetch the dynamically generated tools for Captain Data
  const tools = await toolManager.getTools();
  
  // 4. Bind the tools to the LLM
  const agentWithTools = llm.bindTools(tools);
  
  let messages = [new HumanMessage("Find the VP of Engineering at Acme Corp and list their work experience.")];
  
  while (true) {
    try {
      // Execute the agent step
      const response = await agentWithTools.invoke(messages);
      messages.push(response);
      
      // Check if the agent wants to call a tool
      if (!response.tool_calls || response.tool_calls.length === 0) {
        console.log("Agent finished:", response.content);
        break;
      }
      
      // Execute the requested tools
      for (const toolCall of response.tool_calls) {
        console.log(`Executing tool: ${toolCall.name}`);
        
        const tool = tools.find(t => t.name === toolCall.name);
        const result = await tool.invoke(toolCall.args);
        
        messages.push({
          role: "tool",
          tool_call_id: toolCall.id,
          name: toolCall.name,
          content: JSON.stringify(result)
        });
      }
    } catch (error) {
      // 5. Explicit Rate Limit Handling
      // Truto passes HTTP 429s transparently with standard IETF headers
      if (error.status === 429) {
        const resetSeconds = parseInt(error.headers['ratelimit-reset'] || '60', 10);
        console.warn(`[Rate Limit Hit] Captain Data rejected the request. Sleeping for ${resetSeconds} seconds.`);
        
        // Sleep the thread before retrying
        await new Promise(resolve => setTimeout(resolve, resetSeconds * 1000));
        
        // The loop will naturally retry the LLM invocation or tool execution
        continue;
      }
      
      console.error("Workflow failed:", error);
      break;
    }
  }
}
 
runAutonomousSourcing();

Why This Architecture Matters

By fetching tools dynamically via Truto, you decouple your agent logic from Captain Data's specific API changes. If Captain Data updates the parameters for the search_companies endpoint, Truto updates the JSON schema returned by the /tools endpoint automatically. Your LLM immediately "knows" about the new parameter requirements during its next invocation without you writing a single line of updated integration code.

Furthermore, by enforcing the agent framework to handle the 429 Too Many Requests state using the standardized ratelimit-reset header, you maintain absolute control over the execution lifecycle. The LLM connection does not timeout in a silent proxy queue, and you prevent accidental, expensive double-executions against your Captain Data credit balance.

Moving Beyond the Integration Bottleneck

Connecting AI agents to specialized infrastructure like Captain Data is not a prompting problem; it is a systems engineering problem. The intelligence of your LLM is irrelevant if it is constantly crashing against rate limits, hallucinating pagination boundaries, or failing to parse deeply nested scraping payloads.

By leveraging Truto's proxy architecture and the /tools endpoint, you abstract away the API maintenance burden. You supply the LLM with deterministic JSON schemas, standard error handling, and unified tool names. Your engineering team stops writing custom API wrappers and goes back to building highly capable, autonomous agent workflows.