Understanding AI Agents and Their Growing Importance

AI agents are autonomous software programs designed to perform specific tasks using artificial intelligence (mostly based on LLMs – Large Language Models). They can analyze data, make decisions, and interact with other systems or users.

With these capabilities, they represent a new level automation: Classical automation approaches (e.g. RPA) focus on automating repetitive, rule-based tasks, while AI Agents can operate autonomously, make decisions based on the data they process, learn from experiences and hence automate workflows that have previously required human reasoning.

As technology advances, AI agents are expected to become even more sophisticated, with improved reasoning, planning, and memory capabilities.

Embracing AI agents can lead to significant advancements in process efficiency, customer satisfaction, and overall business performance, making them a valuable asset in the modern enterprise landscape. Therefore, AI agents will become integral components of enterprise IT architectures during the next years, coexisting and collaborating with traditional business systems like ERP (Enterprise Resource Planning), CRM (Customer Relationship Management) or SCM (Supply Chain Management) systems.

The Challenges of Integrating AI Agents

While AI agents offer many benefits, integrating them into existing IT and business processes presents several challenges:

1. Orchestration of multiple agents

Managing multiple AI agents requires a coordinated approach. Without proper orchestration, agents may operate in silos, leading to inefficiencies and potential conflicts in task execution. For instance, in a customer service scenario, one AI agent might handle inquiries while another processes orders. If these agents aren't synchronized, it could result in delayed responses or duplicated efforts. Effective orchestration ensures that AI agents communicate seamlessly, share context, and work collaboratively to achieve business objectives.

2. Embedding into Business Processes

Deploying AI agents isn't just about implementing new technology; it's about integrating them into existing business processes. This ensures that AI agents align with organizational goals. For example, in a sales process, an AI agent might analyze customer data to provide insights, but without embedding this agent into the sales process, the insights may not reach the sales team in time to be actionable. Embedding AI agents effectively requires a thorough understanding of business processes and the ability to adapt workflows to incorporate AI-driven tasks.

3. Tool and Data Integration

AI agents need access to various tools and data sources to function effectively. Integrating these systems can be complex and time-consuming. For instance, an AI agent designed to optimize inventory levels must pull data from sales systems, supplier databases, and warehouse management tools. Without seamless integration, the agent's actions might be based on incomplete or outdated information, leading to suboptimal decisions. Ensuring that AI agents have real-time access to accurate data across platforms is crucial for their effectiveness.

How Scheer PAS Facilitates AI Agent Orchestration

To effectively address the challenges, companies require solutions that offer business process orchestration and integration capabilities. Scheer PAS combines these functionalities in one seamless platform, allowing an efficient and process-driven integration of AI Agents in existing IT landscapes.

End-to-End Process Automation & Orchestration

Scheer PAS allows to design, implement, and manage entire business processes through an intuitive, drag-and-drop BPMN modelling tool. By orchestrating AI agents within these processes, Scheer PAS ensures that each agent operates as an integral component of the end-to-end workflow. This orchestration facilitates seamless collaboration between AI agents, human tasks, and traditional IT systems, leading to more efficient and effective business outcomes.

Application & Data Integration

Scheer PAS offers substantial integration capabilities, enabling AI agents to access and interact with various applications and data sources. This seamless integration ensures that agents have the information they need when they need it. Most important, Scheer PAS allows to control AI agents' interactions with IT systems via defined API access points. By managing these APIs centrally, it can be ensured that AI agents operate within specified parameters, accessing only the data and functions they are authorized to use. This setup not only enhances security but also ensures compliance with organizational policies and regulatory requirements.

Conclusion

Using AI agents for automating tasks and workflows presents both opportunities and challenges. To harness the full potential while maintaining control and coherence across business processes, organizations need platforms like Scheer PAS that offer substantial end-2-end process orchestration and integration capabilities.

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Stellen Sie sich vor, Ihre Integrationen benötigen keine statischen Regeln, Entscheidungen oder Datentransformationen mehr. Und trotzdem geschieht die Ausführung weiterhin intelligent und adaptierbar in einem automatisierten System.

Das ist unsere Zielsetzung für unsere aktuelle Entwicklung an Scheer PAS iPaaS: Die Intelligenz und Autonomie von KI Agenten mit der Welt der Integration und Automatisierung verbinden.

Why We’re Rethinking Integration

Seien wir ehrlich, Integration von Systemen und Prozessautomatisierung hat sich im letzten Jahrzehnt kaum weiterentwickelt. Immer noch definieren wir Datenverknüpfungen, modellieren Prozessabläufe und bauen Integrationslogik. Entweder durch Programmiersprachen oder im No / Low-Code Ansatz. Eine durchaus zeitaufwendige Art, um Geschäftsprozesse zu automatisieren. Auch geraten wir bei aufwändigeren Prozessketten schnell an eine Komplexitätsschwelle, welche uns die Limitierungen von statischen Logikfolgen aufzeigt.

We believe it’s time for a new approach. One that combines the best of traditional rule-based systems with the adaptability of AI.

Durch die Nutzung von KI Agenten, wird es nicht mehr notwendig sein jede Datenumwandlung, Verknüpfung oder Entscheidung zu definieren. Stattdessen werden intelligente Agenten unmittelbar in der Automatisierung von Aufgaben tätig werden und kontextbezogene Entscheidungen treffen. Dadurch vermindern wir die notwendigen Arbeitsschritte und erzielen eine schnellere Delivery der Lösung.

What Are AI agents?

AI agents are autonomous, task-driven components powered by advanced Large Language Models (LLMs). They’re capable of interpreting inputs, analyzing situations, and executing actions—all without needing every step hardcoded. Unlike traditional programs, AI agents can adapt to changing data and requirements in real time, making them suitable for dynamic and complex environments.

You define what the agent should achieve, provide the tools and data it can access, and it takes over from there. Whether it’s syncing records, automating decisions, or performing complex integrations, AI Agents deliver intelligent automation right where it’s needed.

What to Expect

We will provide a Low-Code environment in the Scheer PAS Designer to build and deploy AI agents, giving integration developers the option to use AI agents for their integration and automation tasks. Users will also be able to combine the autonomous, dynamic and intelligent nature of agents with the static and well-defined integration and automation capabilities Scheer PAS is known for. This hybrid approach means you can let AI agents take over where flexibility and real-time decision-making are required—without sacrificing control and reliability.

Key features to work with AI agents will include:

• Definieren von strukturierten Eingabe-/Ausgabeformaten und promptbasierte Aufgabenbeschreibungen
• Choose from built-in tools or connect to external ones via Model Context Protocol (MCP)
• Entwickeln Sie eigene, benutzerdefinierten Tools und stellen Sie diesen den KI-Agenten zur Verfügung
• Auswahl des optimalen LLMs zur Steuerung der Verhaltensmuster der KI
• Run AI Agents standalone or integrate them into BPMN workflows and integration services

Excerpt: The Model Context Protocol (MCP)

Recent developments like the Model Context Protocol (MCP) are addressing integration challenges of AI Agents. MCP is an open standard that enables seamless connections between AI agents and external data sources and tools. MCP serves as a universal adapter, enabling the agents to access the information they need without custom integrations for each data source. Scheer PAS will support the Model Context Protocol.

Potential Use Cases

AI Agents can unlock new possibilities across various industries and scenarios. They’re not limited to one specific field or use case. Whether it's integration tasks or business process automation, AI agents can be applied to use cases that have traditionally relied on static logic and fixed workflows, as well as to those that were previously too complex, dynamic, or unstructured to automate efficiently. Examples are:

• Automatically synchronizing customer data between a Salesforce CRM and an SAP ERP system, resolving format mismatches and enriching records using AI-driven logic
• Extracting meaning from unstructured documents — for example, an AI Agent could process incoming customer service emails, understand the request or issue described, decide on the appropriate response or action, and automatically trigger the necessary updates or workflows in internal IT systems like ticketing, CRM, or ERP platforms.

When is it Coming?

The first version of AI agents in Scheer PAS will be available for selected users in autumn 2025.

We believe this technology will not only change how integration is done with our platform, but across the industry. If you’re an integration developer looking for the next big leap in automation—AI agents are it.

Stay tuned on our LinkedIn channel and our Blog for more updates on this!

The post Eine neue Ära der Integration und Automatisierung: KI-Agenten in Scheer PAS appeared first on Scheer PAS.

At Scheer PAS, we are dedicated to providing a platform that empowers businesses to solve any integration and process automation challenge. That's why with the release of Scheer PAS 24.2, we are excited to introduce the Java xlib DevKit - a feature that empowers users to develop custom functionality and offers the flexibility to solve almost any integration challenge, allowing organisations to seamlessly connect any system-regardless of whether it's an old on-premise solution or a modern cloud-based service.

The Best of Both Worlds: Seamlessly Combine Pro-Code and Low-Code

Scheer PAS has always been about solving the most complex integration and automation challenges. While a pure low-code approach provides an excellent framework for simplifying development, it cannot always deliver the flexibility and advanced functionality required in certain real-world scenarios. In such cases, coding becomes essential to achieve the level of customisation and precision needed.

The new Java xlib DevKit bridges this gap by allowing developers to create custom logic in Java. With this feature, developers can build custom adapters to interface with legacy systems, create specific data transformation logic, or connect with APIs not natively supported by Scheer PAS. This means that almost any integration requirement can be addressed, regardless of how unique or complex the situation may be.

Once compiled into an xlib library, this custom code can be imported into Scheer PAS Designer, making it readily available in the Low-Code development environment. This seamless combination of Pro-Code capabilities and Low-Code simplicity provides a powerful way to build solutions that meet specific business needs. It allows developers to implement advanced functionality that then can be used in the PAS Designer's model-driven interface.

Simplifying Development and Reusability

Another significant advantage of the Java xlib DevKit is how it promotes the reusability of code. By compiling Java-based logic into a standardised xlib format, developers can create custom libraries that can be shared and reused across different projects and teams. This not only accelerates development but also ensures consistency and reduces duplication of effort.

For instance, once a developer builds an integration adapter for a particular system or service, the generated library can be used in future projects by other team members without having to start from scratch. It's a powerful way to create a library of integration assets that scales with the organisation.

How it Works

The integration of the Java xlib DevKit into the Scheer PAS Designer is designed to be as seamless as possible:

1. develop custom logic in Java: Developers can write custom Java code for any functionality, be it integration logic, adapters, or connectors to external systems.

2. compile to xlib: Once the code is developed and tested, it's compiled into the xlib format, making it ready for import.

3. import into Asset Repository: The xlib library can then be easily imported into the PAS Asset Repository, making the custom logic available for use in PAS Designer Projects.

4. drag, drop, and build: Users can now leverage the newly developed Java components within their diagrams, just like any other standard element in the Scheer PAS Designer.

This streamlined process removes much of the friction traditionally associated with combining Pro-code and Low-code, allowing developers and non-developers alike to work together more efficiently. Learn more about how Scheer PAS combines Low-Code and Pro-Code in UI Development here..

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This includes not only the implementation phase, i.e. designing the process with a standardized modelling notation, but especially the way how processes are executed. Most business processes involve asynchronous processing, require the persistence of state information and proper error handling. It is important that the underlying technological concepts of the engines executing the processes support these requirements.

In this blog post, I will explain the basic concept of the Scheer PAS Process Engine.

Process Design and Implementation

The Scheer PAS Designer is the Low-Code tool of the PAS platform to design and develop end to end processes and their implementation. The modelling of the process is done in BPMN (Business Process Model and Notation) which is a standardized graphical notation, that provides a visual representation of the steps and activities involved in a business process. BPMN has the advantage that it is easily understandable by process experts, business users and developers which makes it a powerful notation to not only model but also automate processes. Automating a process with Scheer PAS always means drawing a BPMN in the first step. The figure below shows a simple example using the most common notation elements:

During process implementation, the modeler connects these elements (task, gateway, event, …) with activities to be carried out, which can be pre-built functional building blocks from libraries, user forms , or specific logic captured in a variety of ways (script, mapping diagram, activity diagram), for which Scheer PAS brings its own Low-Code editors.

The Process Execution

Process models are translated by Scheer PAS into semantically equivalent state machines, which is a very powerful approach to define systems with complex and dynamic behaviour. It describes the behaviour of systems that can exist in different states and transitions between them based on certain conditions or events. Execution of these state machines is where the Scheer PAS Runtime comes into play. Related to BPMN process execution, the key elements of this process state machine can be described as follows:

States: A state represents a specific condition or situation that a process instance can be in at a given point in time.

Transitions: Transitions define the movement of a process instance from one state to another in response to a trigger or event.

Events: Events are occurrences or stimuli that can trigger a transition from one state to another.

Actions: Actions are tasks or operations associated with a state or a transition.

Following this state machine approach results in a set of advantages that our process engine can provide when it comes to the execution of a business process:

Asynchronous processing: The state machine concept is inherently asynchronous. This means that it is not necessary for previously started process instances to have ended in order to start a new process instance. Or in other words, several process instances can run in parallel, each one in different stages of the process.

Well-defined state: Each running process instance is in a well-defined state, at all times. It is always clear and transparent which actions have been performed, which events have occurred and what is still to happen.

Data safety: Information about the current state of a process instance as well as instance related process data can be persisted. This means, even if a running process instance is aborted due to unforeseeable reasons (e.g. system crash, power outage, …), the data is not lost, and the process instance can be restarted exactly in the same state with the same data.

In addition to the process state machine for executing the BPMN process, the Scheer PAS process engine also contains a service state machine, which encloses the process state machine.

This service state machine consists of a small set of states surrounding the process state machine and defining the Initialized, Aborted, Error and Done states of the process. This allows transitions from each process state into an Error, Abort or Done state without the need to explicitly model these options in the BPMN. The main advantage that comes with this approach is how potential errors can be handled. Let’s assume the following example:

One activity in the BPMN represents the task to connect to an SAP system and fetch data about a business partner. Due to some reason, the SAP system is not available at the time the process is executed. If the developer has special requirements on error handling and modelled it in the implementation of the process, then this explicit error handling defines what will happen. An example for an explicit handling of the error could be to include a timer event that triggers a reconnect to the SAP system on the next working day. The original process proceeds when the connection can be established.

If, however, there are no specific requirements, and the potential error is not explicitly taken care of – which is mostly the case in practice – then the process transitions automatically in the Error state of the service state machine. This prevents that the process gets stuck in some undefined intermediate state. There are two options now how to proceed from this state: The activity can be repeated manually when the SAP system is available again, or an automatic retry can be configured in which the process engine retries the SAP connection at regular intervals.

In general, this approach allows the developer to identify errors that occur in production, fix the process implementation (including proper error handling if reasonable), redeploy the improved service and restart the process instances from the state where they went into error. All this without losing data or actions that have been processed in the affected instances before the error occurred (-> see Data safety), and without affecting other running instances of the process.

REST Interface for processes

Within the Scheer PAS platform, each implementation of a process is built and deployed as individual service in a Docker container. This has the advantage that each process runs separately and is not affected by errors or maintenance of other services/processes.

To access the process, the Scheer PAS Process engine automatically generates a REST interface for the process state machine. This REST API provides endpoints to gather information on BPMN process instances and their state(s), and to trigger transitions. This approach also provides the option to realize inter-process communication via REST APIs, to build a custom UI layer on top of the process execution or to manage and publish process APIs via the Scheer PAS API Management.

Outlook: Scaling of Processes

With one of the coming Scheer PAS releases we will move to a Kubernetes platform architecture. In addition to separate services for each process, this will allow an easy and automated scaling, i.e. several replicas of the same service / process running in parallel. An individual process can be deployed several times, depending on the required resources, and handle more load in parallel or provide a high availability setup.

The highlight is: Even when there are several replicas of the same service, each executing different instances of the same process, the persisted data and state information will be synchronized between the replicas. This means if one replica of the service crashes, another replica will pick up the started process instances at the point where they were stopped and process them further.

I will explain this unique and powerful concept a bit more in detail in another blog post.

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