Frends Roadmap

A completely new Frends web application that is deployed alongside the main Frends User Interface that is meant to provide a unified API Developer on-boarding and discoverability experience to both internal and external API consumers.

The API portal will support the following use cases and features:

• API Consumer registration and on-boarding

  • Utilizing an identity management system chosen by the customer

• API Products

  • Create and manage API Products in the API Portal that are available to be consumed by the API users

  • API Products are a combination of existing API’s either directly in Frends or managed as passthrough API’s using the Frends Passthrough API Management functionality

• API Discoverability

  • Publish API Products you have configured using the API Portal

• Lightweight CMS

  • Create API marketing and help content using markdown to publish on the API Portal

• Whitelabeling

  • Enable customers and partners to provide lightweight whitelabelling customizations to change for eg. fonts, colors, logos and so on.

An extension to current Frends functionality which allows multiple Frends processes to be chained together in the same process definition by configuring an identification or correlation variables that links the independent process parts together.

An example long-running process functional description:

Process Part 1:

• Triggered with a schedule

• Retrieve a file from an SFTP server

• Write file to a local folder

• Save OrderID to be the correlation ID of the long-running process

• OrderID is parsed from the file name transferred with the SFTP transfer

Process Part 2:

• Establish a file trigger that looks for a fille with the same OrderID present in a different folder

• Read in the new file and transfer it to a separate SFTP server

• Any amount of time can pass between Process Parts 1 and 2.

Key Enhancements:

• Timer BPMN Shape

  • A new BPMN Shape in the Process Editor to stop the execution of a process for a predetermined period of time.

• Wait For Signal BPMN Shape

  • A new BPMN Shape in the Process Editor to stop the execution of a process awaiting for an external signal to continue

  • This signal can come from within the same integration process or from another integration process entirely

  • This will allow also for logical chaining of integration processes

• Send Signal BPMN Shape

  • A new BPMN Shape in the Process Editor to continue the execution of a specified integration process and it's specific stopped instance

• Stateful and Persisted Process

  • All of the data processed within the integration flow can be fully persisted during the wait functionalities described above allowing for fully stateful processes

After many hundreds of votes and customer requests, Frends is finally implementing a visual data mapper to enable users more intuitive and easier way to transform data between any two formats or schemas.

Key Enhancements:

• Automatic Data Schema Detection

  • With this feature, users will be able to provide any structured data for the Visual Data mapper which will infer the underlining schema of the data to allow for easy drag-and-drop mapping of different fields and handle their types correctly

• AI Assisted

  • The Visual Data Mapper will include an AI Assistant that will allow users to ask the AI to create the mapping between two provided data structures automatically.

• Supports Low-Code:

  • The Visual Data Mapper will include the capability to utilize existing Low-Code C# functionality of Frends to further manipulate and process data during the mapping process.

Introducing our upcoming AI Orchestration feature—bringing next-level automation and intelligence directly into your Frends processes. With native, BPMN2.0-compatible AI shapes, you can easily integrate and orchestrate multiple AI steps, chaining Large Language Model (LLM) prompts to fit your specific workflow. By leveraging Azure OpenAI Services as a default platform and pre-trained models from sources like Hugging Face, you can instantly tap into extensive AI capabilities without the overhead of building or training your own models.

• From within the Frends interface, you can visually create decision paths that adapt dynamically based on the AI’s outputs—there is no need to maintain complex session states. Instead, each AI step is orchestrated via Frends BPMN tasks, making automation and prompt adjustments straightforward. This approach ensures maximum flexibility, allowing you to scale and refine your AI logic with minimal effort.
• AI usage tracking and usage constraints are included to give developers clear visibility into costs during development and in production, helping you keep operational expenses in check. Our improved monitoring view shows key insights: AI inputs, outputs, and the AI’s “chain of thought,” so you can fully understand and audit your automation logic at any point.
• By integrating robust AI directly into APIs and business processes, your organization can reduce human intervention, eliminate time-consuming tasks, and accelerate service delivery. McKinsey’s research suggests that up to 60% of tasks can be automated with AI-powered RPA—and with Frends’ new AI orchestration approach, you can potentially reach even higher automation rates. The result? You stay competitive, lower costs, and effortlessly scale operations without introducing human bottlenecks.

Stay tuned for this transformative feature in our public roadmap—it’s coming soon to revolutionize how you automate, integrate, and innovate with AI.

We're evolving the Frends Native AI Shape into a full Agentic AI execution model. This means you can now provide the AI Shape with a custom “AI Toolbox” — a list of pre-built Frends processes that the LLM can call as actions when reasoning through a task.

For example, you can expose your existing Frends APIs like getCustomerFromSalesforce or insertOrderToSAP to the LLM AI Shape. These become callable tools in AI’s decision-making logic, all orchestrated with standard BPMN 2.0. AI chooses which actions to execute, using your own APIs and automation logic. It's like giving your process a brain — with hands.

Customer Benefits:

• 🛠️ Composable AI Actions: Expose your APIs or automations as reusable actions for LLM to reason with.

• ⚙️ Agentic Orchestration: AI can plan and decide what steps to take using your own logic and systems.

• 🧩 Low-Code, High-Value: Use familiar BPMN 2.0 and Frends triggers to create AI-driven processes — no AI retraining needed.

• 🚀 Use Case Ready: For example, from a single prompt, AI checks if a customer exists (via Salesforce), inserts an order (into SAP), and handles errors — autonomously.

Why it matters:
This is the bridge from static AI tasks to AI Agents. You’re not just prompting AI — you’re letting it act.

For more information about Autonomous AI Agents see the following Gartner research: https://www.gartner.com/en/articles/intelligent-agent-in-ai

Passthrough APIs provide a way of more easily configure API calls to be forwarded to backend system and ways of monitoring these calls

• Passthrough API functionality to enable users to easily configure existing API’s to be governed by Frends API Management without having to have an underlining Frends process attached to the API.

  • The passthrough API will have simplified configuration capabilities to modify:

    • HTTP Headers

    • Authentication methods

    • URL paths and parts

• Improved API Monitoring capabilities

  • Introduce new API Monitoring views to show more API relevant data such as HTTP response codes as well as better ways to view the overall API health and execution statistics

API Policies

We are introducing Unified API Policies, designed to centralize and streamline access management for all API’s while preparing for the upcoming release of the Frends API Portal.

Key Enhancements:

• Unified Policy Configuration: With Unified API Policies, you can easily configure access restrictions and throttling for all your APIs through a single, user-friendly mechanism.

• Throttling and Access Control: Tailor API Policies to meet your specific needs. Define access limits and throttling rules to ensure optimal API usage and prevent abuse.

• Adaptable to Any Authentication Method: API Policies are versatile and can be applied to APIs regardless of the authentication method used, providing consistent control and management.

New Authentication Method: Private Application Registration

Additionally we are introducing a new authentication method to work with the reworked API Policies: Private Application Registrations.

Key Enhancements:

• Secure Authentication: Private Application Registrations supersede API Keys, offering a more secure and modern authentication solution.

• Static JWT Tokens: These tokens are essentially static JSON Web Tokens (JWT) containing user-provided data about the API consumer, alongside digital signatures to ensure data integrity.

We are introducing an advanced feature within Frends that enables users to create Code shapes efficiently through natural language prompts. This enhancement streamlines the process of writing C# code to perform complex integration tasks within your integration processes.

Key Enhancements:

• Contextual Information: System prompts are integrated to provide contextual details for process configuration, including environment variable names, process variables, trigger variables, and access to task results.

• Seamless Integration: When opted in the Process Editor seamlessly connects to our LLM service (Azure Open AI) for effortless use.

• Tenant-Specific Configuration: For security and flexibility, each Frends tenant will utilize it’s own dedicated instance of the LLM model for context and session security.

We are introducing a powerful new capability within Frends that enables users to generate process descriptions and documentation with ease, using the Frends Large Language Model (LLM). This feature simplifies the process of creating detailed descriptions for your Processes.

Key Highlights:

• Description Creation: Users can initiate the process by simply clicking a button to have the Frends LLM generate an initial Process description and documentation based on the BPMN Process designed, variables and Tasks used as well as any user provided documentation.

• Fine-Tuning with Additional Prompts: After receiving the initial response, users have the flexibility to provide additional prompts to further refine and customize the generated description.

Building on the previous feature of LLM Code Generation we are also introducing a feature that allows users to configure Tasks seamlessly through natural language prompts. This simplifies the process of configuring Task parameters, creating low-code mapping configurations and utilizing Task results and other variables when configuring Frends tasks.

Key Highlights:

• Effortless Task Configuration: Users can effortlessly configure Tasks by describing the required functionality for the task using plain language, eliminating the need for manual configuration.

• Contextual Continuity: This feature retains the chat context, enabling users to iteratively enhance Task configurations within the same conversation as well as test out their configuration at any time.

• Tenant-Specific Configuration: For security and flexibility, each Frends tenant will utilize it’s own dedicated instance of the LLM model for context and session security.

We are introducing a valuable enhancement to the Frends Process Editor by incorporating Decision Model and Notation (DMN) functionality. DMN provides an efficient means of mapping data and decision-making, as described in detail here.

Key Enhancements:

• DMN Shape Integration: With this feature, we are introducing a dedicated DMN shape within the Frends Process Editor. This shape will allow users to create input maps following the table structure outlined in the Wikipedia article.

• Mapping to Frends Variables: Users can easily associate DMN inputs with various Frends variables, streamlining the data mapping process.

• Output Generation: The result of the DMN shape will provide corresponding outputs based on the input configurations ready to be used in following integration tasks and steps.