Connect Fishbowl Advanced to AI Agents: Automate Sourcing & Sales

You want to connect Fishbowl Advanced to an AI agent so your system can independently read inventory levels, generate purchase orders, move stock between locations, and orchestrate complex manufacturing workflows. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to build and maintain a custom, hallucination-prone integration from scratch.

Giving a Large Language Model (LLM) read and write access to a manufacturing and inventory ERP like Fishbowl Advanced 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 Fishbowl Advanced to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Fishbowl Advanced 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 Fishbowl Advanced, bind them natively to an LLM using frameworks like LangChain, LangGraph, CrewAI, or the Vercel AI SDK, and execute complex supply chain workflows. For a deeper look at the architecture behind this approach, refer to our research on [architecting AI agents and the SaaS integration bottleneck](/architecting-ai-agents-langgraph-langchain-and-the-saas-integration-bottleneck/).

The Engineering Reality of the Fishbowl Advanced 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.

Fishbowl Advanced 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.

The Hybrid SQL and CSV-over-JSON Data Model

Standard LLMs are trained to expect flat, intuitive JSON REST APIs. Fishbowl Advanced deviates significantly from this pattern for many core operations.

First, many read operations in Fishbowl do not have dedicated REST endpoints. Instead, the API requires you to execute raw SQL queries against the underlying database to retrieve records like sales orders or products. Asking an LLM to generate raw, syntactically perfect SQL on the fly is a massive hallucination risk.

Second, writing data for objects without REST endpoints requires using Fishbowl's "Import data via JSON" capability. This is not a standard JSON payload. It requires a highly specific format where the payload is a JSON array of rows, and the first row must be the CSV header. Furthermore, the import_name parameter must precisely match the case-sensitive default name of the CSV import/export with hyphens replacing spaces (e.g., Sales-Order-Details).

If you expose the raw Fishbowl API to an LLM, the model will constantly fail to format these SQL and pseudo-CSV payloads correctly. A unified tool layer abstracts these quirks, giving the agent a predictable, strongly typed JSON schema to target.

Authentication Sessions and Rate Limiting

Fishbowl Advanced requires explicit session creation and management. You must call a login endpoint, handle Multi-Factor Authentication (MFA) challenges if enabled, and manage session timeouts. Truto handles this session lifecycle behind the scenes, logging in on connection and refreshing every 30 minutes, keeping session management completely out of your agent's prompt context.

When it comes to API rate limiting, Fishbowl will enforce limits on how much data you can query or write. It is critical to understand how this is handled at the network layer. Truto does not automatically retry, throttle, or absorb rate limit errors. When the upstream Fishbowl API returns an HTTP 429 Too Many Requests, Truto passes that error directly back to the caller.

What Truto does do is normalize the upstream rate limit information into standardized IETF headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset). This means your agent framework only needs to look for one set of headers across all integrations. The responsibility for implementing exponential backoff and retry logic rests entirely on your agent's execution loop.

High-Leverage Fishbowl Advanced Tools for AI Agents

To build effective supply chain and manufacturing agents, you need to arm them with the right operations. Instead of overwhelming the LLM context with hundreds of generic endpoints, you filter for high-leverage tools that map directly to business logic.

Here are the hero tools provided by Truto for orchestrating Fishbowl Advanced.

Execute Custom Data Queries

list_all_fishbowl_advanced_data_queries

Because many standard read operations lack REST endpoints, this tool is the backbone of your agent's read capabilities. It allows the agent to execute a data query against the database using SQL. It strictly enforces SELECT statements (Truto rejects anything else) to prevent accidental data corruption. The agent uses this to read sales orders, customers, and product tables.

"Run a SQL query to find all sales orders in Fishbowl where the statusId is equal to 2, and return the order ID and number."

Import Data via JSON (The Universal Writer)

create_a_fishbowl_advanced_import

This tool allows the agent to create or update records that have no dedicated REST write endpoint (like specific sales orders, customers, or parts). The agent passes a JSON array of rows where the first row acts as the CSV header. The tool requires a heavily validated import_name parameter to ensure the payload routes to the correct Fishbowl module.

"Create a new customer record by executing an import. Use 'Customer' as the import name and format the payload as a JSON array starting with the required header row."

Manage Inventory Levels

list_all_fishbowl_advanced_inventory

Before making sourcing or manufacturing decisions, an agent must know what is currently in stock. This tool searches for inventory, with Truto automatically handling the complex pageNumber and pageSize pagination up to the total available pages.

"Check the current inventory levels for part ID 84920 across all warehouses to see if we have enough raw materials for the upcoming production run."

Execute Inventory Moves

create_a_fishbowl_advanced_inventory_move

Warehouse automation requires physical items to be logically moved in the ERP. This tool allows the agent to move inventory from one location to another, requiring the specific part_id and tracking metrics.

"Move 500 units of part ID 443 from the Receiving Dock location to the Main Storage location in Fishbowl."

Generate Manufacture Orders

create_a_fishbowl_advanced_manufacture_order

When inventory falls below safety thresholds, the agent can autonomously spin up production. This tool creates a new manufacture order, requiring strict adherence to the configurations schema.

"Generate a new manufacture order using configuration ID 992 to produce 50 units of the finished good."

Orchestrate Purchase Orders

create_a_fishbowl_advanced_purchase_order

For external sourcing, the agent can generate POs directly. This tool creates a purchase order and requires the vendor object to be defined. It is heavily utilized in autonomous procurement workflows.

"Create a new purchase order for vendor ID 12 for 1,000 units of standard aluminum framing."

Manage Manufacture Order Memos

create_a_fishbowl_advanced_manufacture_order_memo

Communication on the factory floor is often handled via memos attached to specific orders. This tool allows the agent to append notes, instructions, or status updates to a manufacture order.

"Add a memo to manufacture order ID 4500 stating that the production timeline has been delayed by two days due to material shortages."

For the complete tool inventory, detailed JSON schemas, and parameter requirements, refer to the Fishbowl Advanced integration page.

Building Multi-Step Workflows

To move beyond simple queries, you must bind these tools to a framework capable of executing multi-step reasoning loops. In production, this requires fetching the tools dynamically from Truto's /tools API, binding them to your LLM, and wrapping the execution in a loop that can handle upstream API failures (like HTTP 429 Rate Limits).

This pattern works across any modern agent framework, including LangChain, LangGraph, CrewAI, and the Vercel AI SDK. Here is a concrete implementation using the Truto Langchain.js SDK.

import { ChatOpenAI } from "@langchain/openai";
import { TrutoToolManager } from "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.FISHBOWL_ACCOUNT_ID,
  trutoAccessToken: process.env.TRUTO_API_KEY,
});
 
async function runAgent() {
  // 3. Fetch specific tools from Truto's API
  const tools = await toolManager.getTools({
    methods: ["read", "write"], // Filter for actionable tools
  });
 
  // 4. Bind the heavily typed JSON schemas to the LLM
  const llmWithTools = llm.bindTools(tools);
 
  const messages = [
    new HumanMessage("Check inventory for part ID 84920. If we have less than 100 units, create a purchase order for vendor ID 12 for 500 units.")
  ];
 
  let isComplete = false;
 
  // 5. The Agent Execution Loop
  while (!isComplete) {
    const response = await llmWithTools.invoke(messages);
    messages.push(response);
 
    if (response.tool_calls && response.tool_calls.length > 0) {
      for (const toolCall of response.tool_calls) {
        try {
          const tool = tools.find((t) => t.name === toolCall.name);
          const result = await tool.invoke(toolCall.args);
          
          messages.push({
            role: "tool",
            content: JSON.stringify(result),
            tool_call_id: toolCall.id,
          });
        } catch (error) {
          // 6. Mandatory Error Handling (Specifically HTTP 429)
          if (error.response && error.response.status === 429) {
            // Truto passes the 429 and normalizes the headers.
            // You MUST implement the backoff here.
            const resetTime = error.response.headers['ratelimit-reset'];
            console.warn(`Rate limit hit. Waiting until ${resetTime}...`);
            // Implement your sleep/wait logic here
            messages.push({
              role: "tool",
              content: "Error: Rate limit exceeded. I should wait and try again.",
              tool_call_id: toolCall.id,
            });
          } else {
            messages.push({
              role: "tool",
              content: `Error executing tool: ${error.message}`,
              tool_call_id: toolCall.id,
            });
          }
        }
      }
    } else {
      isComplete = true;
      console.log("Agent finished:", response.content);
    }
  }
}
 
runAgent();

In this architecture, Truto guarantees that the tool definitions (tools) accurately reflect the upstream Fishbowl schema, completely abstracting the complex authentication and pagination mechanics. Your application code is only responsible for the business logic and managing the state of the agent loop.

Workflows in Action

When you give an agent contextual memory and access to these tools, it stops being a simple chatbot and becomes an autonomous supply chain operator. Here are three concrete workflows demonstrating how the agent chains Fishbowl Advanced tools to solve complex problems.

1. Autonomous Inventory Rebalancing

A logistics coordinator needs to ensure that the primary assembly line never runs out of key components, requiring constant monitoring of warehouse bins and manual transfers in the ERP.

"Audit the inventory for part ID 5520 across all locations. If the 'Assembly Floor' location has fewer than 200 units, move enough stock from the 'Bulk Storage' location to bring the Assembly Floor back up to 500 units."

Step-by-step execution:

1. The agent calls list_all_fishbowl_advanced_inventory filtering for part ID 5520.
2. It analyzes the returned JSON array, identifying the quantities in 'Assembly Floor' (e.g., 150) and 'Bulk Storage' (e.g., 1000).
3. The LLM calculates the deficit (500 - 150 = 350).
4. The agent calls create_a_fishbowl_advanced_inventory_move with a payload specifying the part_id, source location, destination location, and a quantity of 350.

Result: The user receives a confirmation that 350 units were logically transferred in Fishbowl, keeping the assembly line supplied without human intervention.

sequenceDiagram
    participant User as User
    participant Agent as AI Agent
    participant Truto as Truto Tool Layer
    participant Fishbowl as Fishbowl Advanced
    
    User->>Agent: "Rebalance part ID 5520..."
    Agent->>Truto: Call list_all_fishbowl_advanced_inventory
    Truto->>Fishbowl: GET Inventory API
    Fishbowl-->>Truto: Inventory Data
    Truto-->>Agent: JSON Response (150 on floor)
    Agent->>Truto: Call create_a_fishbowl_advanced_inventory_move
    Truto->>Fishbowl: POST Move API (Qty: 350)
    Fishbowl-->>Truto: 201 Created
    Truto-->>Agent: Move Confirmation
    Agent-->>User: "Moved 350 units to Assembly Floor."

2. Intelligent Procurement and Vendor POs

Procurement managers spend hours identifying shortages and manually keying purchase orders into the system. An agent can automate the entire requisition phase.

"Find all raw materials associated with vendor ID 12 that currently have an inventory level of zero. For each item found, generate a single consolidated purchase order requesting 1,000 units of each."

Step-by-step execution:

1. The agent calls list_all_fishbowl_advanced_data_queries utilizing a raw SQL SELECT statement to join the vendor and parts tables, filtering for zero quantity.
2. The query returns a list of specific part IDs mapped to vendor 12.
3. The agent formats an array of line items based on the results.
4. The agent calls create_a_fishbowl_advanced_purchase_order, passing vendor ID 12 and the constructed line items.

Result: The user is provided with the newly generated Purchase Order ID, entirely eliminating the manual data entry and cross-referencing process.

3. Custom Reporting via SQL and CSV Import

Often, sales teams need custom status updates that require bulk updates to records that lack standard REST endpoints.

"Run a SQL query to find all sales orders currently marked as 'Pending Fulfillment'. Then, update their custom field 'Priority' to 'High' using the JSON data import method."

Step-by-step execution:

1. The agent calls list_all_fishbowl_advanced_data_queries to execute a SELECT statement retrieving the IDs of pending sales orders.
2. The agent structures a JSON array where the first object is the header row required by Fishbowl (e.g., "OrderNumber", "CustomField-Priority").
3. The agent formats the remaining objects with the order numbers and the string "High".
4. The agent calls create_a_fishbowl_advanced_import, passing the exact string Sales-Order as the import_name along with the JSON array payload.

Result: The agent successfully bridges the gap between raw SQL reads and batch CSV-style writes, updating dozens of orders instantly and reporting back the successful import ID.

The Strategic Value of Abstracted Tooling

When you stop writing defensive integration code to handle SQL generation, CSV-JSON formatting, and authentication sessions, you can focus entirely on the reasoning capabilities of your AI agent.

By leveraging a unified tool layer, your LLM operates against a predictable, heavily validated schema. This drastically reduces hallucinations, speeds up execution time, and allows your engineering team to build complex supply chain automations that actually work in production.