Uag

The Universal Agent Gateway (UAG) leverages the Model Context Protocol (MCP) to enable in-process agentic automation using Form.io. It provides to an AI Agent the same thing that our JavaScript Renderer provides to a human; an interpretation of the Form JSON schema into an understandable format. In the case of UAG, it transforms the Form JSON model into an AI readible markdown format so that the Agent can easily understand the purpose and structure of the data that needs to be collected.

There are two primary scenarios that the UAG enables:

• Unstructured content conversion to deterministic structured data: This capability uses the JSON schema of a Form.io form to produce a deterministic structured data object provided a Natural Language input.

• Agentic Form.io Workflows: UAG provides an AI Agent the ability to take existing submission data + "context" criteria in order to prompt an AI Agent to provide its own data and analysis within a workflow scenario. The following example, which shows a college application process using a Form.io application form, shows how this primary scenario works.

For more information on Agentic workflows, checkout the dedicated Agentic Workflows section

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One of the many benefits that the UAG has to offer is the ability to inject Dynamic Context into an AI processing cycle. This is achieved from the dynamic nature of the Form JSON schemas that can easily be modified and deployed to any enviornment without the need to update your application or re-train the agents within the process. Because of this, dynamic context changes can be injected seamlessly into an existing running process which will automatically update the behaviors of the Agents utilzing the UAG.

Try it out!

The quickest way to try out the UAG run the Local Example which will spin up a local instance of Form.io Open Source and UAG. This can then be connected to an AI agent, which will have the ability to interact with the included example forms. The local example can be run entirely free, with no trial or license required.

Go to Local Example »

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Agentic Workflows

One of the more powerful features of the UAG is the agent_provide_data tool. This tool provides the ability to instruct a generically trained agent how to analyze existing submission data, and then produce its own data by following a configurable Criteria. This behavior historically could only be achieved using a specifically trained agent, which does not provide any benefits of dynamic configurability that the Form.io platform offers. This tool is able to achieve this goal by providing a generally trained agent with the necessary "context" it needs to accurately produce its own data as part of an automated workflow. This feature is particularly helpful if you wish to utilize the UAG within an Agentic Workflow, where the AI Agent is capable of understanding structured data, and then contribute its own data by following the configured Criteria "context" provided by the UAG.

For example, let's suppose you wish to automate the backend administration behind a College Application Process. In this example, a potential student submits an application that consists of many different fields of data, such as Academnics, Extra curricular activities, Honors, Volunteer work, as well as possibly written Essays. Historically, these applications would be reviewed by an administrator in order to assess the candidates qualifications for acceptance. With the agent_provide_data tool, it is now possible to automate this process as the following diagram illustrates.

To achieve this feat, the agent_provide_data tool utilizes the following information, which is then fed to the Generally trained agent to produce its own submission data.

• Existing Submission Data: In order for the agent to be able to contribute its own data, it must first have an existing submission to be used as the data that it will analyze according to the configured Criteria.
• Criteria: This is a piece of content that provides the agent the Criteria to follow when analyzing the data, but also provides instructions on how the agent should populate the Required Fields.
• Required Fields: These are the form fields which the Agent is required to fill out as part of the agent_provide_data process.

Setup

To configure a form to use the agent_provide_data tool, you must first designate a section of your form that will be read and used by the AI Agent. This is similar to what you would see in a form that says "For Office Use Only", but instead of a Human contributing to the values of this section, it will be an automated AI Agent. There are two types of fields that can be added to a form to configure it for use by the agent_provide_data tool: Criteria and Agent Fields

Agent "Criteria" component

The first thing that needs to be configured is a special Content Component that instructs the AI Agent how to interpret the data. For example, here is a Criteria content block that was written to instruct an AI Agent on how to assess the submission of a College Application Essay.

The goal of this content is to be written as you would write an instruction manual for a new employee who needs to learn how to analyze and understand the submission data that is provided. It is also used to instruct the AI Agent on how to populate the form values that are configured for that criteria. Once this criteria is written, It will then need to be "flagged" as a UAG field by adding a property called uag and the value of that property is to be thought of as the persona of the Agent. This provides the ability to have more than one agent assume different roles as it analyzes the submission data to provide their own values. In addition, the "criteria" content will also need to be provided a uagField property with the value equal to criteria. Below is what a properly configured uag criteria content component looks like.

Once you have added this Criteria Content Component to your form, the next object is to add the fields you wish for the Agent to populate. These are called the Agent Fields.

Agent Fields

Anywhere in your form, you can drag and drop fields that can be flagged as Agent fields. Using the example above, imagine your form has a "For Office Use Only" section (maybe a Panel). Within this panel, the first thing you see is a Content Component with instructions on how the following fields should be filled out. This is the Criteria we just described. The next thing that follows are the fields that the Agent needs to populate. Any field that with a property of uag set to the same value as the Criteria content component will be used as the Agent fields.

For example, lets suppose that we have created a form for College Application essays. Below the Criteria may be some fields that the Agent needs to "score" the essays according to the provided Criteria. This may look like the following...

In order to "flag" each of these fields as Agent Fields, you simply need to add the uag property with the same persona flag that was giving to the Criteria content.

Nested Components It is also possible to "flag" a collection of fields with the uag property. This can be done by wrapping all of these fields within a Nested Component (such as Panel, Container, Fieldset, etc), and then all the fields within this nested component will be added to the context of that agent with that persona.

Multiple Personas per form

It is also possible to achieve multiple personas per-form. This is very helpful if there are several isolated evaluations that need to occur within a process. For example, if we look at the flow chart diagram shown above, we can see a College Application Form. The first agentic evaluation occurs when the form is submitted by the applicant, where their application is assessed to be accepted or not. From that point, IF the applicant is accepted, a completely different evaluation needs to occur to award Financial Aid or even Scholarships to the applicant. These would require completely different criteria to assess the submission data differently from one another.

To achieve this, you simply need to "flag" the components uag property value differently with the persona that applies for that field. For the example above, you would add a Content component to assess the if the applicant should be accepted, and then provide the following uag property to that content. uag="application". Any fields that need to be filled out by the AI Agent for application approval would also be flagged with the property uag="application". Then, in a different section of the form, you would create a separate Content component with the criteria for the financial admin AI Agent. This content would be flagged with the property uag="finance". Any fields that the finance admin AI Agent need to fill out would also be flagged with uag="finance". This provides a truely flexible and dynamic AI engagement where multiple generically trained agents can be "taught" dynamically how to read and interpret data that is only relevant to that part of the agentic process.

Integrations

In order to a aid in the "agentification" of the UAG, we have also provided some "integrations" that can be used to directly communicate with the Generically trained AI Agents to utilize the UAG to accomplish automated AI Agent behaviors. This involves providing a single API endpoint that is capable of triggering the AI Agent process along with the connections to your deployed UAG. These API endpoints can then be triggered using a simple Webhook action within a form to start an Agentic process as the result of a Form submission. Here is a diagram of how specific integrations can be used to achieve a single API call to accomplish an agentic task.

These integrations can be found within the integrations folder. The following integrations are provided to enable automated Agentic workflows.

• Claude Integration

Please click on one of these integration links to read how they work.

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Technical Overview

The UAG can be thought of as consisting of many layers of functionality. Each layer is broadly either serving to fetch dynamic context, or submit deterministic data structures. The following layers are leveraged within the UAG:

1. MCP Interface: The entry point of the UAG is the MCP interface that sits between the AI agent and the tools that are executed on the server.
2. Authorization: As part of the MCP specification, every interaction between the AI agent and the backend service must pass through an Authorization process leveraging OIDC (PKCE) authentication. The UAG takes this process one step further, however, by adding an OIDC (PKCE) authentication layer on top of the existing authentication flexibilities that Form.io already provides. Because of this, it is possible to authenticate with the UAG if you are using SAML, Form.io Authentication, OIDC, or even Custom Authentication processes through the Form.io platform.
3. Pre-defined MCP Tools providing Dynamic Context: The UAG utilizes the MCP to provide a number of pre-defined tools that provide AI agents with dynamic context based on the Forms and Resources marked as compatible within the Form.io platform. These tools are described in more detail in a section below.
4. Custom Tools (provided by Custom Modules): This enables developers to introduce any new tools that can leverage Form.io forms and submission data structures to provide additional capabilities introduced to the AI agents. This would allow for custom agent interactions to achieve certain goals for specific use cases.
5. Custom Actions (provided by Custom Modules): Enables developers to create composable middleware that will be executed once all data provided by the AI agent has been sanitized and validated. This provides the ability to interface the data collected from AI agents with any backend system or workflow.
6. Built-in Actions: In addition to any custom actions introduced via a Module, the UAG supports a handful of existing Actions that can be assigned to any form or resource. Actions such as the Webhook that will send a REST API call to any endpoint once any submission has been made by an AI agent. This can be used to point to any serverless function to instigate backend workflows that are triggered from the interactions from AI agents.
7. Pre-defined Forms and Resources (provided by Custom Modules): Allows a developer to release a module that provides default data structures to the AI agent. This would be useful if a developer wishes to create a module that achieves a specific goal, where the data structures required for that goal need to follow a pre-determined set of fields provided by a form or resource. For example, if a developer wishes to release a CRM module for the UAG, then it would make sense that the module would also provide the default Customer and Company resources that commonly accompany most CRM implementations.
8. Custom Database (provided by Custom Module): By default, the UAG performs the data collection and retrieval via a "Submission Proxy", whereas the data is submitted via REST API to either the Enterprise Server project or the Open Source server. This, however, could be modified to send the data directly to and from a Database. This can be achieved via the Database Interface provided from the UAG. For more information, see the Module documentation. This feature does require that the UAG is connected to the Enterprise Server.

Here is a diagram to understand how all of these layers are organized to make up the UAG.

Pre-defined MCP Tools providing Dynamic Context

The following tools provided by the UAG can be described as follows:

Tool Name	Tool Description
get_forms	Provides the AI agent with a list of available forms. It will only return forms that have been tagged uag.
get_form_fields	When the AI agent infers the user intends to use a specific form, this tool provides the agent with a high level overview of all the fields needed (along with the field data path) to submit that form.
get_field_info	Once the fields have been determined using get_form_fields, this tool provides specific information about the requested fields, such as validation, conditionals, input formats, etc. This tool instructs the AI agent on how to format and structure the data that is sent to the MCP server.
collect_field_data	Provides the AI agent with a mechanism to dynamically collect the required information from the user. It can parse the user's input into multiple fields at once, and will identify additional inputs from the user if needed. This tool supports complex and structured data collection and is compatible with nested or multi-value fields like nested forms, data grids, etc.
confirm_form_submission	This tool is used to provide a summary of all data collected before a submission is made to the form.
submit_completed_form	Provides the AI agent with the ability to submit all of the data collected from the user to create the form submission.
find_submissions	Enables the agent to parse a user's natural language request into a query for a submission, or a specific field of a particular submission.
submission_update	Provides the AI agent with the ability to update an existing submission, either by supplying unfilled fields or updating existing ones if allowed. Provides the AI agent with the context of the existing field values, allowing inline changes or edits.
agent_provide_data	Enables an AI Agent to be able to analyze existing submission data according to rules defined with a configurable Criteria. It will then instruct the Agent to provide generated data for fields configured for a specific persona using the submission_update tool. Please read the Agentic Workflows section for more information about this toolset.
fetch_external_data	This tool enables an AI Agent to pull external data into a form workflow through the use of either a Resource component, Select with URL, or a Data Source component.

Custom Modules

While the UAG can be used as a stand-alone system to enable the interaction between AI agents and dynamic JSON forms, the true power of this platform will be realized when developers extend the capabilities of this platform to solve industry specific use cases through the use of Custom Modules and Tools. It is possible for a developer to create a Module that introduces a number of custom tools, actions, and pre-defined resources and forms to achieve interactions with industry specific technologies. They are able to achieve this custom integration capabilities through the use of the following mechanisms:

1. Pre-defined Form and Resources: Using the Form.io template system, a custom module can include a template export of a Form.io project that includes a number of Forms, Resources, and Actions (as well as any Roles and Permissions associated with them). By using the module, this will then automatically "register" these assets so that the AI agent is immediately aware of those assets.
   For example, imagine a module includes a Customer resource with "First Name", "Last Name", and "Email" fields. If you then simply ask an AI agent, "I would like to add a new Customer" the agent will automatically be aware of the context for the customer data structures and know exactly what your intent is when making that request.
2. Custom Actions: Actions can be thought of as composable middleware functions that allow for configurable Express.js middleware functions to be attached to any Form and Resource, and then to be executed once the data being submitted by an AI agent has passed through the proper data validations and sanitization. These can be used to provide custom integrations to backend processes and workflows.
3. Custom Tools: In addition to providing custom actions, a Module can also introduce custom MCP Tools to provide bespoke interaction capabilities between the UAG and the AI agents for any industry specific purposes.
4. Custom Authentication: A module can also modify the "authentication" landing page to assist with any custom requirements that are needed around Authentication.
5. Behavior Overrides: A module also has the ability to "override" any default behavior from the UAG. This ensures that whomever is using a certain module can "tweak" certain tools and responses to achieve better results as it pertains their their industry specific needs.

Form.io aims to enable developers to build and contribute their own custom modules to the broader community through GitHub and NPM.

Using a Custom Module

Once a custom module is developed, anyone can install that module (via NPM) and then easily use your module in the following way...

_{This is just an example.}

import { UAGServer } from '@formio/uag';
import { ExampleUAG } from '@example/uag';
import Express from 'express';
try {
    (async function () {
        const server = new UAGServer();
        server.use(ExampleUAG);
        const app = Express();
        app.use(await server.router());
        const port = process.env.PORT || 3200;
        app.listen(port, () => {
            console.log(`Form.io UAG server running on port ${port}`);
            console.log(`Visit http://localhost:${port} to access the application`);
        });
    })();
} catch (error) {
    console.error('Failed to start server:', error);
    process.exit(1);
}

To get started in building your own custom module, go to the help documentation at Modules Readme.

Deploying UAG

The following sections describe the process of creating a running instance of UAG, connecting it to the Form.io Platform, and configuring an AI agent to use it.

This documentation focuses on using UAG with Form.io Open Source. For documentation on using UAG with Form.io Enterprise, refer to https://help.form.io/uag.

Runtime Environments

There are currently two run-time environments that work with the UAG: -Docker -Node.js (Express)

Docker

Running the UAG in Docker enables a wide range of deployment options into common hosting environments such as AWS and Azure. Additionally, it allows for the use of common orchestration runtimes such as Kubernetes and Docker Compose. The container that you will run the UAG is as follows:

formio/uag

This container can be run in two ways:

• Docker-Compose.yml - The recommended method, deploys with of a Docker Compose multicontainer.
• Docker Run - a standalone container using the common docker run command.

Docker Compose (Recommended)

The recommended way to launching the UAG is through Docker Compose. This enables you to orchestrate several of the containers to run within a single instance to provide a more seamless and simple way of managing your deployments. Here is a simple example of how to run both the Form.io UAG + Form.io OSS server on the same instance.

docker-compose.yml

version: "3.8"
services:
  mongo:
    image: mongo
    restart: always
    volumes:
      - ./data/db:/data/db
    environment:
      - MONGO_INITDB_ROOT_USERNAME
      - MONGO_INITDB_ROOT_PASSWORD
  formio:
    image: formio/formio:rc
    restart: always
    links:
      - mongo
    depends_on:
      - mongo
    environment:
      PORT: 3000
      DEBUG: formio.*
      NODE_CONFIG: '{"mongo": "mongodb://mongo:27017/formio-oss", "jwt": {"secret": "CHANGEME"}, "mongoSecret": "CHANGEME"}'
      ROOT_EMAIL: admin@example.com
      ROOT_PASSWORD: CHANGEME
      ADMIN_KEY: CHANGEME
    ports:
      - "3000:3000"
  formio-uag:
    image: formio/uag
    restart: always
    links:
      - formio
    depends_on:
      - formio
    environment:
      PORT: 3200
      DEBUG: formio.*
      PROJECT: http://formio:3000
      ADMIN_KEY: CHANGEME
      JWT_SECRET: CHANGEME
      JWT_EXPIRE_TIME: 525600
      LOGIN_FORM: http://localhost:3000/employee/login
      BASE_URL: http://localhost:3000
    ports:
      - "3200:3200"

This can be run by typing the following...

docker compose up -d

Docker Run

Here are some examples of running the UAG using the docker run command.

UAG pointed to an Open Source server

docker run -d \
  -e "PROJECT=https://forms.mysite.com" \
  -e "ADMIN_KEY=CHANGEME" \
  -e "JWT_SECRET=CHANGEME" \
  -e "BASE_URL=https://forms.mysite.com" \
  -e "LOGIN_FORM=https://forms.mysite.com/user/login" \
  -e "PORT=3200" \
  --restart unless-stopped \
  --network formio \
  --name formio-uag \
  -p 3200:3200 \
  formio/uag

UAG pointed to an Form.io Enterprise Server

docker run -d \
  -e "PROJECT=https://forms.mysite.com/myproject" \
  -e "PROJECT_KEY=CHANGEME" \
  -e "UAG_LICENSE=YOUR-LICENSE" \
  -e "JWT_SECRET=CHANGEME" \
  -e "PORTAL_SECRET=CHANGEME" \
  -e "BASE_URL=https://forms.mysite.com" \
  -e "LOGIN_FORM=https://forms.mysite.com/myproject/user/login" \
  -e "PORT=3200" \
  --restart unless-stopped \
  --network formio \
  --name formio-uag \
  -p 3200:3200 \
  formio/uag

Once it is running, you can then navigate to the following to access both the OSS Deployment + UAG Server

• http://localhost:3000: The Form.io OSS Server
• http://localhost:3200: The UAG Server

In both the Node.js runtime environmnt as well as Docker, the way to control the UAG is through the use of Environment Variables and Modules.

Environment Variables

This module can be configured in many ways. One of those ways is through the use of Environment Variables, which are documented as follows.

Variable	Description	Example
PROJECT	The API Endpoint to either an Enterprise project endpoint, or the OSS server url.	http://localhost:3000
PROJECT_KEY	(Enterprise Only) Either a Project API Key (for Form.io Enterprise) or the ADMIN_KEY for Open Source.	CHANGEME
ADMIN_KEY	(Open Source Only) Allows you to provide the ADMIN_KEY to install and connect to the OSS Server.	CHANGEME
UAG_LICENSE	The license to run the UAG against a Form.io Enterprise Deployment.
PROJECT_TTL	The project cache TTL in seconds. This controls the amount of time to check for any "changes" that have been made to the project to refresh any forms and resources within the UAG.	900
PORT	The port you wish to run the server on.	3200
DEBUG	Variable used to perform debug logs of server activity	formio.*
PORTAL_SECRET	Enterprise Only: Allows you to connect to the UAG from the Form.io Enterprise Portal.	CHANGEME
JWT_SECRET	A secret used to generate and validate JWT tokens generated through the authentication process of the UAG. This does not need to match the JWT_SECRET of the Enterprise Server that it is connected to.	CHANGEME
JWT_EXPIRE_TIME	The expiration for the jwt secret.	3600
MONGO	(Enterprise Only) Allows you to connect the UAG directly to a mongo database, rather than having to redirect the submissions to the Form.io Submission APIs.
MONGO_CONFIG	JSON configuration for the Node.js Mongo Driver.
BASE_URL	The public URL that the UAG is hosted on. This allows for proper OIDC authentication and allows for the authentication callbacks to point to the correct url.	https://forms.yoursite.com
LOGIN_FORM	The public URL to the Login Form JSON endpoint.	https://mysite.com/project/user/login
CORS	The cors domain, or the JSON configuration to configure the "cors" node.js module cross domain resource sharing.	.

Project Cache and TTL

By default, the UAG uses a TTL to "fetch" any of the project assets and register them and cache them into the UAG. What this means is that if you make any changes to your underlying project forms and resources (either directly or through a deployment), you need to wait a maximum of the TTL setting (in seconds) to see any updates that have been made. For example, if the TTL is set to 900 (or 15 minutes), and you make a change to any forms and resources, you must wait at most 15 minutes to see any of these changes take effect into the UAG. It should be noted that when a "refresh" occurs, an API call to the "export" method is called on the underlying project endpoint. This is a processor intensive API call, so it is not recommended to set the TTL to any value less than 60. A value of 0 is interpreted as "No TTL", which means the only way to refresh the project forms and resourcs, a UAG reboot is necessary.

Here is a table explaining how this parameter can be used.

Environment Variable	Value	Result
PROJECT_TTL	0	No TTL (refresh) will occur. This can be used to "disable" any refresh and ensure the ONLY way to refetch updated forms and resources, you must reboot the UAG server.
PROJECT_TTL	60	Check for changes every minute
PROJECT_TTL	3600	Check for changes every hour

Running on Public Domain

In order to run the UAG on a public domain, it is very important to provide the proper configurations so that any AI agent can properly authenticate. There are 3 different "domain" environment variables that matter, and it is important to understand how to configure them depending on your use case:

PROJECT

The PROJECT environment variable is used to establish a connection from the UAG server to the project endpoint (for Enterprise) or OSS base url. This does NOT need to be a public DNS entry, but rather a URL that connects the UAG container to the Server container. The following examples illustrate how this would be configured.

Local connection to OSS Server

If you are using a local connection, such as within a Docker Compose file, you can configure the PROJECT environment variable to point directly to the local url as follows.

docker-compose.yml

version: "3.8"
services:
  formio:
    image: formio/formio:rc
    restart: always
    environment:
      PORT: 3000
      ADMIN_KEY: CHANGEME
  formio-uag:
    image: formio/uag
    restart: always
    links:
      - formio
    depends_on:
      - formio
    environment:
      PROJECT: http://formio:3000
      ADMIN_KEY: CHANGEME

In this example, we have Docker Compose launching the OSS Form.io container with the ADMIN_KEY set for this deployment, the UAG is connected using http://formio:3000 which is the local Docker network name (provided using the "links" property in the docker compose file).

Local Connection to Enterprise Server Project

If you are using the Enterprise Form.io server within a local environment to the UAG, then you will need to ensure that the UAG connects to an independent project using the PROJECT_KEY as follows.

docker-compose.yml

version: "3.8"
services:
  formio-enterprise:
    image: formio/formio-enterprise
    restart: always
    environment:
      PORT: 3000
  formio-uag:
    image: formio/uag
    restart: always
    links:
      - formio-enterprise
    depends_on:
      - formio-enterprise
    environment:
      PROJECT: http://formio-enterprise:3000/myproject
      PROJECT_KEY: CHANGEME

Public DNS

If your project does not reside on the same network as the UAG, you can provide the domain name as the PROJECT as follows.

docker-compose.yml: Connected to Enterprise (formio/formio-enterprise)

version: "3.8"
services:
  formio-uag:
    image: formio/uag
    restart: always
    environment:
      PROJECT: https://forms.mydomain.com/myproject
      PROJECT_KEY: CHANGEME

docker-compose.yml: Connected to Open Source (formio/formio)

version: "3.8"
services:
  formio-uag:
    image: formio/uag
    restart: always
    environment:
      PROJECT: https://forms.mydomain.com
      ADMIN_KEY: CHANGEME

BASE_URL

The BASE_URL is used to communicate to the AI agent the public domain that is hosting the UAG server. This value is provided within the .well-known definitions for the OIDC (PKCE) authentication. If this is not correct, then the AI agent will not be able to authenticate into the UAG.

This needs to be the publicly accessible domain that you are hosting your UAG.

For example, BASE_URL: https://forms.mysite.com.

LOGIN_FORM

This is the publicly accessible URL to the Login form of your Project or OSS deployment. This provides the URL that is loaded when the user navigates to {{ BASE_URL }}/auth/authorize. If you navigate to this URL, and the page says that you cannot load the form, then this is because your LOGIN_FORM environment variable is not pointing to the correct form JSON endpoint of your project.

For example:

• Enterprise Example: LOGIN_FORM: https://forms.mysite.com/myproject/user/login
• OSS Example: LOGIN_FORM: https://forms.mysite.com/user/login

Node.js (Express):

With the Node.js environment, you can import the UAG within a locally running Node.js and Express.js environment. This works by first importing the UAG module and "use"ing it within an Express.js application. First, you will install the UAG inside of your Node.js Express application like the following.

npm install --save @formio/uag

or

yarn add @formio/uag

You can then mount the UAG within your Express application, as seen in the following example:

import 'dotenv/config';
import Express from 'express';
import { UAGServer } from '@formio/uag';
try {
    (async function () {
        const server = new UAGServer();
        const app = Express();
        app.use(await server.router());
        const port = process.env.PORT || 3200;
        app.listen(port, () => {
            console.log(`Form.io UAG server running on port ${port}`);
            console.log(`Visit http://localhost:${port} to access the application`);
        });
    })();
} catch (error) {
    console.error('Failed to start server:', error);
    process.exit(1);
}

There is also a way to extend the functionality of the UAG through the use of modules, which is documented in the Modules Readme

Using with Form.io Enterprise Server

When using the UAG with the Form.io Enterprise Server, you unlock several benefits with regards to managing the Forms and Resources within the UAG. Some of the features that you gain with our Enterprise Server include:

• Form Revisions - Our form revision system provides a way to keep track of any changes made to any forms, and associate those changes with the submission data that is submitted against those revisions. This feature enables you to ensure that if any form schemas change, that the data submitted for that form correlates with the revision of the form in which it was submitted. In addition to this powerful feature, you can also leverage Form Revisions as a method to "roll-back" any mistakes or regressions caused by form changes. Instead of having to re-train or un-train an AI agent with any schema, you simply revert to previous revisions and the AI agent will adjust accordingly.
• Submission Revisions - Submission revisions provides a way to keep track of any data changes that have been made to a submission and provides information on who changed the data. This combined with the power of the UAG provides any system the ability to audit any changes in data made by both AI agents as well as humans in the workflow processes.
• Stage Versions and Deployments - Stage versions provides a way to create a "tag" version across all forms and resources within a project. This ensures that you can stamp a point in time where your AI agent (which may consume many different forms and resources) interfaces with your whole project in a deterministic way. It also provides a much more elegant "roll-back" mechanism where the entire project of forms and resources can be versioned and deployed independently.

Running the UAG against the Enterprise Server

In order to run the UAG against an Enterprise Server, you need to provide a few Environment variables that are different from the OSS runtime of this module. The following environment variables are required to run against an Enterprise Project.

Variable	Description	Example
PROJECT	For the Enterprise Server, this points to the Project Endpoint you wish to bind the UAG to.	https://mydeployment.com/myproject
PROJECT_KEY	An API Key for that project, provided within the Project Settings	CHANGEME
UAG_LICENSE	This is the license provided to you from the Form.io License team. Contact support@form.io to acquire a "temporary" or full license.

Once you have these environment variables in place, you should be able to run the UAG pointed to your Enterprise Project.