Connect Infor M3 to AI Agents: Sync ERP and Warehouse Operations

You want to connect Infor M3 to an AI agent so your system can independently orchestrate warehouse movements, sync inventory balances, process customer orders, and manage financial ledgers based on historical context. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to build a custom ERP integration layer from scratch.

Giving a Large Language Model (LLM) read and write access to your Infor M3 instance is an engineering challenge (see our guide on connecting AI agents to NetSuite or SAP). You either spend sprints deciphering M3 MI transactions, building XML/JSON wrappers, and managing OAuth lifecycles, or you use a managed infrastructure layer that handles the boilerplate for you. If your team uses ChatGPT, check out our guide on connecting Infor M3 to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Infor M3 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 Infor M3, bind them natively to an LLM using frameworks like LangChain, LangGraph, CrewAI, or the Vercel AI SDK, and execute complex enterprise resource planning (ERP) workflows. 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 Infor M3 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 ERP systems, this approach collapses.

Infor M3's API introduces several specific integration quirks 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.

The Movex API (MI) Transaction Model

Infor M3 does not expose a purely RESTful, resource-oriented API. Instead, it relies on Movex API (MI) programs. Every operation requires invoking a specific MI program (e.g., OIS100MI for Customer Orders or MMS200MI for the Item Toolbox) and a specific transaction within that program (e.g., AddOrderHead, LstItmWhsByWhs).

Standard LLMs expect flat, intuitive REST actions (read more about LLM function calling for integrations). When an agent wants to create an order, it naturally attempts to execute a single POST /orders payload with all line items nested inside. Infor M3 rejects this. The API requires a sequence of stateful calls: first OIS100MI/AddOrderHead, extracting the returned order number, and then looping over OIS100MI/AddOrderLine for each item. If you expose raw HTTP tools to your LLM, it will invariably hallucinate a nested JSON payload that M3 cannot parse.

Strict Organizational Context Headers

Nearly every transaction in Infor M3 requires strict execution context, specifically the Company (CONO) and Division (DIVI). If an agent attempts to retrieve a warehouse balance but drops the DIVI parameter, the MI transaction will either fail cryptically or, worse, execute against the wrong operational facility, generating silent data corruption.

Fixed Date and Parameter Formats

M3 expects dates and financial parameters in highly specific formats (e.g., YEA4 for fiscal years, exact string lengths for certain identifiers). If an agent attempts to pass an ISO-8601 timestamp to a field expecting a specific M3 period integer, the transaction will fail. Managing this validation inside the LLM context window eats up tokens and introduces massive instability.

Why a Unified Tool Layer Matters for Agent Safety

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

Direct API tools push provider quirks into the LLM's context. The model has to remember that M3 needs OIS100MI/AddOrderHead instead of a standard POST request, that Divisions must be specified on every call, and that dates must be formatted correctly. Every one of those quirks is a hallucination waiting to happen.

A unified tool layer collapses the Infor M3 API behind standardized JSON schemas. Your agent sees infor_m_3_ois_100_mi_customer_order_add_order_head with a strict definition of required inputs. That gives you concrete safety wins:

1. Deterministic input validation. Every tool has a strict JSON schema. Invalid arguments (like a missing CONO or poorly formatted date) are rejected by Truto before they hit the ERP. A broken tool call fails fast instead of generating a corrupted ledger entry.
2. Smaller attack surface for hallucination. The LLM only ever chooses from stable function names. It never invents MI program names or guesses at transaction limits.
3. Decoupled integration maintenance. When Infor updates an MI program or modifies a transaction signature, Truto maps those updates at the integration level. Your agent's code does not change.

Hero Tools for Infor M3

Truto provides a comprehensive mapping of Infor M3's MI programs as isolated, executable tools. The agent framework fetches these tools dynamically. Here are the most critical tools for building autonomous supply chain and finance workflows.

Create Customer Order Head

Tool: infor_m_3_ois_100_mi_customer_order_add_order_head

This tool executes OIS100MI/AddOrderHead. It initializes a new customer order and requires the customer number (CUNO). The agent must capture the resulting order number to pass to subsequent line-item tools.

"Create a new customer order for customer number 99012. Ensure the order type is standard and capture the returned order number so we can add line items."

Add Customer Order Line

Tool: infor_m_3_ois_100_mi_customer_order_add_order_line

Executes OIS100MI/AddOrderLine. It appends an item to an existing order. The agent must provide the order number (ORNO) and the item number (ITNO).

"Add 50 units of item 'SKU-7781' to order number 500123."

Check Warehouse Balance

Tool: infor_m_3_mms_200_mi_item_toolbox_get_itm_whs_bal

Executes MMS200MI/GetItmWhsBal. Agents use this to verify inventory availability across facilities before attempting to confirm customer orders or plan logistics routes. Requires Warehouse (WHLO) and Item Number (ITNO).

"Check the current stock balance for item 'SKU-7781' in warehouse 'MAIN-01'. Let me know if the allocable balance is below our safety threshold."

Update Stock Balance (Stocktake)

Tool: infor_m_3_mms_301_mi_report_stocktake_upd_stock_bal

Executes MMS301MI/UpdStockBal. Allows an agent to programmatically reconcile inventory levels after a physical count or sensor-triggered cycle count. Requires the physical inventory number (STNB) and warehouse data.

"Update the physical stock count for stocktake number 9044. Set the new quantity for item 'SKU-7781' to 420 units."

Retrieve Supplier Invoice

Tool: infor_m_3_aps_200_mi_supplier_invoice_get_invoice

Executes APS200MI/GetInvoice. Crucial for autonomous AP (Accounts Payable) workflows. The agent can pull invoice data, match it against purchase orders, and trigger payment approvals. Requires the financial year (YEA4), journal number (JRNO), and journal sequence number (JSNO).

"Fetch the supplier invoice details for journal number 8820 in fiscal year 2026 so we can verify it against the master PO."

Retrieve Customer Basic Data

Tool: infor_m_3_crs_610_mi_customer_get_basic_data

Executes CRS610MI/GetBasicData. An agent uses this to pull CRM-level data from the ERP, checking credit limits, payment terms, and billing addresses before initiating transactions. Requires Customer Number (CUNO).

"Pull the profile data for customer 99012. I need to verify their current credit limit and default shipping terms before we process their new bulk order."

For the complete inventory of Infor M3 tools, required inputs, and JSON schemas, see the Truto Infor M3 integration page.

Workflows in Action

When you chain these tools together, your agent graduates from a simple chatbot to an autonomous ERP operator. Here is how specific personas use these workflows in production.

1. Autonomous Order Fulfillment (Supply Chain Ops)

Supply chain managers waste hours manually validating inventory before pushing orders through. An agent can automate this validation and entry cycle entirely.

"We just received an urgent email request from Customer 99012 for 200 units of SKU-7781. Check if we have enough allocable stock in warehouse MAIN-01. If we do, create the order and add the line item."

Execution Steps:

1. The agent calls infor_m_3_crs_610_mi_customer_get_basic_data to ensure Customer 99012 is active and has no credit holds.
2. The agent calls infor_m_3_mms_200_mi_item_toolbox_get_itm_whs_bal for MAIN-01 and SKU-7781.
3. Seeing 500 units available, the agent calls infor_m_3_ois_100_mi_customer_order_add_order_head and captures the returned ORNO (e.g., 55102).
4. Finally, it calls infor_m_3_ois_100_mi_customer_order_add_order_line passing ORNO: 55102, ITNO: SKU-7781, and the quantity.

Result: The customer order is staged in M3, and the user receives a confirmation message with the new order ID, zero manual data entry required.

2. Supplier Invoice Reconciliation (Finance)

AP teams manually match supplier invoices to purchase orders. An agent can monitor incoming invoices, retrieve the ERP records, and flag discrepancies.

"Audit supplier invoice journal 8820 for fiscal year 2026. Check the total against the approved purchase order and flag any variance over 5%."

Execution Steps:

1. The agent calls infor_m_3_aps_200_mi_supplier_invoice_get_invoice using YEA4: 2026 and JRNO: 8820.
2. It extracts the associated purchase order number (PUNO) from the invoice record.
3. The agent calls infor_m_3_pps_200_mi_purchase_order_information_and_get_head to pull the original PO totals.
4. It runs an internal calculation to compare the invoice total to the PO total.

Result: The agent either approves the match or outputs a detailed variance report, allowing the finance team to manage by exception.

Building Multi-Step Workflows

To build these workflows, you need to fetch Truto's dynamically generated tools and bind them to your LLM.

Every integration in Truto represents an underlying product's API. Truto maps these APIs into a unified format and generates JSON schemas for every method (like GetItmWhsBal or AddOrderHead). By querying the /integrated-account/<id>/tools endpoint, you retrieve an array of definitions that frameworks like LangChain, LangGraph, or the Vercel AI SDK can ingest directly.

The Architecture

sequenceDiagram
    participant User as User
    participant Agent as AI Agent
    participant Truto as Truto Tools API
    participant Infor as Infor M3 API

    User->>Agent: "Check stock and create order"
    Agent->>Truto: GET /integrated-account/{id}/tools
    Truto-->>Agent: Returns JSON Schema for M3 tools
    Agent->>Agent: Bind tools to LLM (e.g., .bindTools())
    Agent->>Truto: Call mms_200_mi_item_toolbox_get_itm_whs_bal
    Truto->>Infor: Execute MMS200MI/GetItmWhsBal
    Infor-->>Truto: Stock data
    Truto-->>Agent: JSON Response
    Agent->>Truto: Call ois_100_mi_customer_order_add_order_head
    Truto->>Infor: Execute OIS100MI/AddOrderHead
    Infor-->>Truto: OK (ORNO: 55102)
    Truto-->>Agent: JSON Response
    Agent-->>User: "Order 55102 created."

Binding Tools to the Agent

If you are using TypeScript and LangChain, the TrutoToolManager handles tool registration.

import { ChatOpenAI } from "@langchain/openai";
import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
import { TrutoToolManager } from "truto-langchainjs-toolset";
import { ChatPromptTemplate } from "@langchain/core/prompts";
 
// Initialize the LLM
const llm = new ChatOpenAI({
  model: "gpt-4o",
  temperature: 0,
});
 
// Initialize Truto tools for your specific Infor M3 connected account
const trutoManager = new TrutoToolManager({
  trutoApiKey: process.env.TRUTO_API_KEY,
  integratedAccountId: process.env.INFOR_M3_ACCOUNT_ID,
});
 
async function runAgent() {
  // Fetch the M3 tools dynamically from the Truto API
  const tools = await trutoManager.getTools();
 
  const prompt = ChatPromptTemplate.fromMessages([
    ["system", "You are an autonomous ERP assistant. Use the provided Infor M3 tools to orchestrate supply chain and finance tasks."],
    ["human", "{input}"],
    ["placeholder", "{agent_scratchpad}"],
  ]);
 
  // Bind the tools to the agent
  const agent = createToolCallingAgent({
    llm,
    tools,
    prompt,
  });
 
  const executor = new AgentExecutor({
    agent,
    tools,
  });
 
  // Execute a multi-step workflow
  const result = await executor.invoke({
    input: "Check the stock balance for SKU-7781 in warehouse MAIN-01. If it has more than 100 units, create an order for customer 99012 and add 50 units to the order."
  });
 
  console.log(result.output);
}

Handling API Rate Limits

When your agent chains multiple transactions together rapidly (e.g., looping through 50 lines to add to an order), it will inevitably hit Infor M3's concurrency or rate limits.

Crucial Architectural Note: Truto does not retry, throttle, or apply automatic backoff on rate limit errors. When the upstream Infor M3 API returns an HTTP 429, Truto passes that 429 error directly back to the caller.

Truto normalizes the upstream rate limit information into standardized IETF headers:

• ratelimit-limit: The maximum number of requests allowed in the current window.
• ratelimit-remaining: The number of requests left in the current window.
• ratelimit-reset: The time at which the rate limit window resets.

Your agent execution loop is responsible for reading these headers and applying backoff. If you are building a custom LangGraph node to execute Truto tools, your error handling logic must look like this:

async function executeTrutoToolWithBackoff(toolCall: any, maxRetries = 3) {
  let attempt = 0;
 
  while (attempt < maxRetries) {
    try {
      // Execute the tool (abstracted)
      const response = await invokeTool(toolCall);
      return response;
    } catch (error: any) {
      if (error.status === 429) {
        // Read Truto's standardized headers
        const resetTime = error.headers['ratelimit-reset'];
        const sleepDuration = calculateSleep(resetTime);
 
        console.warn(`Rate limit hit. Sleeping for ${sleepDuration}ms...`);
        await new Promise(resolve => setTimeout(resolve, sleepDuration));
        attempt++;
        continue;
      }
      // Rethrow if it's not a rate limit error (e.g., a 400 Bad Request)
      throw error;
    }
  }
  throw new Error("Max retries exceeded for M3 tool execution.");
}

By forcing the orchestration layer to handle the backoff, Truto ensures your system maintains complete control over execution state, preventing hidden timeouts and hanging LLM context windows.

Final Thoughts

Connecting an AI agent to an ERP like Infor M3 shifts your application from a passive dashboard to an active participant in your users' supply chain. However, exposing raw Movex API transactions to an LLM will guarantee system instability and hallucinated payloads.

By routing agent traffic through Truto's /tools endpoint, you abstract away the structural quirks of the M3 API. The LLM receives clean, standardized JSON schemas for every transaction, complete with input validation and deterministic error passing. You spend your engineering cycles optimizing agent logic, not maintaining ERP API boilerplate.