Connect Navan SCIM to AI Agents: Automate Directory Data Lookups

You want to connect Navan SCIM to an AI agent so your system can autonomously provision users, audit directory data, and manage travel policies based on enterprise identity context. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to build and maintain a custom Navan SCIM integration from scratch.

Giving a Large Language Model (LLM) read and write access to your enterprise directory is an engineering headache. You either spend weeks building, hosting, and maintaining a custom connector, or you use a managed infrastructure layer that handles the boilerplate for you. If your team uses ChatGPT, check out our guide on connecting Navan SCIM to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Navan SCIM 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 Navan SCIM, bind them natively to an LLM using frameworks like LangChain, LangGraph, CrewAI, or the Vercel AI SDK, and execute complex identity and travel operations 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 Agent Safety

Before writing a line of integration code, you must decide what layer your agent talks to. This choice determines how safe, deterministic, and scalable your production system will be.

Direct API tools - exposing raw Navan SCIM endpoints directly to the agent - look convenient during a hackathon, but they push provider quirks directly into the LLM's context window. The model has to remember the intricacies of SCIM 2.0 schemas, understand how to format deeply nested JSON structures, and manage raw pagination cursors. Every one of those quirks is a hallucination waiting to happen.

A unified tool layer collapses these complexities behind a stable, semantic schema. Your agent sees simple, deterministic function names like list_all_navan_scim_users instead of raw HTTP methods mapped to urn:ietf:params:scim:schemas:core:2.0:User. That gives you concrete safety wins:

1. Smaller attack surface for hallucination. The LLM only ever chooses from a stable list of predefined function names. It never invents arbitrary API paths or query operators.
2. Deterministic input validation. Every tool provided by Truto has a strict JSON schema. Invalid arguments are rejected before they ever hit the Navan API, ensuring a broken tool call fails fast instead of corrupting remote directory states.
3. Decoupled execution. The agent reasons about the intent (e.g., "find a user"), while Truto handles the underlying HTTP request, authentication headers, and data normalization.

The Engineering Reality of the Navan SCIM API

Giving an LLM access to external data sounds simple in a prototype. You write a Node.js function that makes a fetch request and wrap it in an @tool decorator. In production against complex identity systems like Navan SCIM, this approach collapses rapidly.

The Navan SCIM API introduces several specific integration challenges that break standard REST assumptions. If you hardcode these interactions into your agent, you will spend your sprints writing defensive integration code instead of improving your model's reasoning capabilities.

The SCIM 2.0 Schema Strictness Trap

Navan strictly adheres to the SCIM 2.0 protocol. Standard LLMs are trained to expect flat, intuitive JSON objects. When an agent wants to update a user's department, it naturally attempts to send a payload like {"department": "Engineering"}.

Navan SCIM will reject this immediately. The API requires a heavily nested structure defined by specific URNs. An update payload often requires wrapping data in urn:ietf:params:scim:schemas:extension:enterprise:2.0:User and defining specific attribute formatting. Standard LLMs frequently truncate these URNs, hallucinate shorthand keys like enterprise_user, or misplace the nesting hierarchy. Without a proxy layer to normalize this, your agent will constantly throw 400 Bad Request errors.

The Pagination Math Problem

SCIM 2.0 does not use cursor-based pagination (e.g., next_page_token), which LLMs handle reasonably well by just passing a string back. Instead, SCIM relies on offset-based pagination using startIndex and count, where startIndex is notoriously 1-based, not 0-based.

To page through a directory, an agent must correctly calculate startIndex = previous_startIndex + count. LLMs are notoriously bad at maintaining accurate arithmetic state across multiple reasoning steps. By page three or four, the agent will almost certainly miscalculate the offset, leading to skipped records or infinite loops. Truto abstracts this away entirely. The Proxy API handles the pagination state, allowing the LLM to simply request the "next page" without doing the math.

Rate Limits and 429 Handling

When syncing large directory structures or auditing hundreds of user profiles, your agent will inevitably hit Navan's rate limits. It is critical to understand that Truto does not retry, throttle, or apply automatic backoff on rate limit errors.

When Navan returns an HTTP 429 Too Many Requests, Truto passes that error directly back to the caller. However, Truto does something crucial for the agent developer: it normalizes the upstream rate limit information into standardized headers per the IETF specification. Regardless of how Navan formats its rate limit response, Truto guarantees you will receive:

• ratelimit-limit
• ratelimit-remaining
• ratelimit-reset

The caller (your agent framework) is strictly responsible for reading the ratelimit-reset header and implementing the retry and backoff logic. Do not expect the integration layer to absorb these errors invisibly - you must design your agent loop to pause execution when it hits a limit.

sequenceDiagram
    participant Agent as AI Agent
    participant Framework as LangChain / Framework
    participant Truto as Truto Unified API
    participant Navan as Navan SCIM

    Agent->>Framework: Call list_all_navan_scim_users
    Framework->>Truto: GET /proxy/navanscim/users
    Truto->>Navan: GET /scim/v2/Users?startIndex=1
    Navan-->>Truto: HTTP 429 Too Many Requests
    Truto-->>Framework: HTTP 429 (Normalized Headers)<br>ratelimit-reset: 60
    Framework-->>Agent: Tool Execution Error (Rate Limited)
    Note over Agent,Framework: Agent must wait 60s<br>and retry the operation

Hero Tools for Navan SCIM

Truto provides a dynamic /tools endpoint that exposes all the Methods defined on the Resources for an integration. These are automatically formatted as JSON schemas ready for LLM consumption. Here are the highest-leverage tools available for Navan SCIM workflows.

List All Navan SCIM Users

This tool allows the agent to retrieve a list of users from the Navan directory. It abstracts the startIndex pagination, allowing the agent to filter users based on query parameters without needing to understand the underlying SCIM 2.0 URL encoding.

"Fetch a list of all active users in the Navan directory and identify anyone who does not have an assigned cost center."

Get Single Navan SCIM User by ID

When an agent needs deep context on a specific employee - perhaps to verify their travel policy tier or active status - this tool fetches the complete, nested SCIM profile for a single user using their unique SCIM ID.

"Retrieve the complete directory profile for the Navan user with ID 987654321 and tell me their current management reporting chain."

Create Navan SCIM User

This tool enables the agent to provision a new user in the Navan system. It handles the enforcement of required SCIM 2.0 core attributes (like userName, name.formatted, and emails), ensuring the agent provides valid payloads before the API is touched.

"Provision a new Navan user account for Alice Smith using alice.smith@company.com and assign her to the Engineering department."

Update Navan SCIM User

This tool allows the agent to issue full updates (PUT) to a user record. This is crucial when an employee changes departments or requires a complete overhaul of their enterprise extension attributes.

"Update Bob Jones's Navan profile to reflect his recent promotion. Change his title to Senior Director and update his cost center to CC-404."

Patch Navan SCIM User

Generating valid SCIM JSON Patch operations (op, path, value) is extremely difficult for an LLM. This tool provides a structured schema for partial updates, allowing the agent to safely disable a user or modify a single attribute without replacing the entire user object.

"Deactivate the Navan user account for Charlie Davis effective immediately by patching the active status to false."

To view the complete tool inventory, including precise query schemas and resource definitions, visit the Navan SCIM integration page.

Workflows in Action

Exposing individual tools is only the first step. The real power of connecting Navan SCIM to an AI agent lies in chaining these tools together to execute multi-step identity operations. Here are real-world use cases showing exactly how an agent leverages these tools.

Use Case 1: Automated Offboarding and Access Audits

When an employee leaves a company, IT admins need to ensure their access to travel and expense platforms is revoked immediately. An AI agent can handle this process autonomously.

"Audit the Navan directory for the user sarah.connor@cyberdyne.com. If she exists and is currently active, deactivate her account and return a confirmation summary."

1. The agent calls list_all_navan_scim_users with a filter parameter for the email address sarah.connor@cyberdyne.com.
2. The tool returns the user array. The agent extracts the specific Navan SCIM ID from the response.
3. The agent calls patch_navan_scim_user using the extracted ID, passing the SCIM patch payload to set active: false.
4. The agent formulates a natural language response confirming the exact timestamp the account was deactivated.

Use Case 2: Travel Policy and Cost Center Synchronization

Finance teams frequently update cost center codes, which need to be reflected in Navan to ensure travel expenses route correctly. An agent can reconcile these changes based on a natural language prompt.

"Find all users in the 'Marketing' department in Navan. Update their cost center extension attribute to 'MKT-2026'."

1. The agent calls list_all_navan_scim_users filtering by the department attribute.
2. The tool returns a paginated list of matching users.
3. The agent iterates over the results, calling update_navan_scim_user for each individual ID, mapping the new cost center value into the urn:ietf:params:scim:schemas:extension:enterprise:2.0:User structure.
4. The agent handles the aggregation of results and reports back any failures or rate limit pauses it encountered during the loop.

Building Multi-Step Workflows

To build an autonomous workflow, you need to programmatically fetch the Navan SCIM tools from Truto and bind them to your LLM framework. The following example demonstrates how to achieve this using TypeScript, standard fetch for the tools, and LangChain's agent abstraction. This exact pattern applies equally to Vercel AI SDK, CrewAI, or any other modern framework.

flowchart TD
    A["Initialize Agent<br>Framework"] --> B["Fetch Tools from<br>Truto API"]
    B --> C["Bind Tools to<br>LLM (e.g., .bindTools)"]
    C --> D["Agent Reasoning<br>Loop"]
    D --> E["Agent Decides<br>to Call Tool"]
    E --> F["Execute Request<br>via Truto Proxy"]
    F --> G{"HTTP Status?"}
    G -->|200 OK| H["Return JSON<br>to Agent"]
    G -->|429 Too Many Requests| I["Read ratelimit-reset<br>Wait and Retry"]
    H --> D
    I --> F

Fetching and Binding Tools

First, you fetch the definitions from the /integrated-account/<id>/tools endpoint. Truto returns descriptions and strictly typed JSON schemas for every Navan SCIM method.

import { ChatOpenAI } from "@langchain/openai";
import { AgentExecutor, createOpenAIToolsAgent } from "langchain/agents";
import { TrutoToolManager } from "truto-langchainjs-toolset";
 
async function runNavanWorkflow() {
  // 1. Initialize the Truto Tool Manager with your API key
  const trutoManager = new TrutoToolManager({
    apiKey: process.env.TRUTO_API_KEY,
  });
 
  // 2. Fetch all available tools for the specific Navan integrated account
  const navanTools = await trutoManager.getToolsForIntegratedAccount(
    process.env.NAVAN_ACCOUNT_ID
  );
 
  // 3. Initialize the LLM (e.g., GPT-4o)
  const llm = new ChatOpenAI({
    modelName: "gpt-4o",
    temperature: 0,
  });
 
  // 4. Bind the fetched tools to the LLM natively
  const llmWithTools = llm.bindTools(navanTools);
 
  // 5. Create the agent executor
  const agent = await createOpenAIToolsAgent({
    llm: llmWithTools,
    tools: navanTools,
    prompt: customPromptTemplate,
  });
 
  const executor = new AgentExecutor({
    agent,
    tools: navanTools,
    maxIterations: 10,
  });
 
  // 6. Execute the workflow
  const result = await executor.invoke({
    input: "Find all active users in the engineering department and list their SCIM IDs."
  });
 
  console.log(result.output);
}

Handling Rate Limits in the Agent Loop

Because Truto passes HTTP 429s directly to the caller, your execution layer must be resilient. While the AgentExecutor in LangChain handles basic tool execution, you should implement a custom wrapper or use framework-specific error handling to catch 429s, read the ratelimit-reset header, and pause execution.

Here is a conceptual example of how you handle the normalized rate limit headers returned by Truto when executing a tool call:

async function executeWithBackoff(toolCallFn) {
  let retries = 3;
  while (retries > 0) {
    try {
      return await toolCallFn();
    } catch (error) {
      if (error.status === 429) {
        // Truto normalizes the upstream response into standard IETF headers
        const resetTime = error.headers.get('ratelimit-reset'); 
        const waitSeconds = resetTime ? parseInt(resetTime, 10) : 10;
        
        console.log(`Rate limited by Navan SCIM. Waiting ${waitSeconds} seconds...`);
        await new Promise(resolve => setTimeout(resolve, waitSeconds * 1000));
        retries--;
      } else {
        throw error; // Throw standard 4xx and 5xx errors back to the agent
      }
    }
  }
  throw new Error("Max retries exceeded for Navan SCIM tool call.");
}

By handling the backoff explicitly, you prevent the agent from burning tokens by continuously hallucinating retry attempts, and you respect the upstream constraints of the Navan API.

Moving from Script to System

Connecting Navan SCIM to an AI agent requires more than just API keys and fetch requests. It requires a resilient architecture that isolates the LLM from the eccentricities of SCIM 2.0 schemas, handles pagination deterministically, and provides a framework for programmatic rate limit recovery.

By leveraging Truto's /tools endpoint, you abstract away the integration boilerplate. Your agent interacts with a clean, semantic set of tools, while Truto handles the protocol translation, authentication routing, and header normalization. This allows your engineering team to focus on what actually matters: improving the agent's reasoning capabilities and building out high-value identity workflows.