What are compensation events?

One of the outstanding features provided by Camunda 8 is its ability to easily handle business transactions that went awry. This workflow pattern allows businesses to define compensatory actions and “undo” steps in a workflow if some operations in the sequence fail. In today’s complex business processes spanning across distributed architectures, we can find many examples where executed tasks need to be undone to reflect the real-world state.

For example, when a system runs financial transactions, money transfers, or online credit applications, it needs to confirm the request, process the transaction, and update the account. If any of the stages are not completed, the system has to be capable of rolling the previous steps back to avoid incorrect transactions or data inconsistencies. Namely, it would be inappropriate to leave the credit request in the confirmed state if the loaned money didn’t arrive in the client’s account, say, because of a failure of a microservice.

So, in business operations, Camunda’s compensation events enable the automatic reversal of actions that were successfully completed, but their results and side effects are no longer seen as desirable. Thus, the compensation feature ensures the process is preserved in a consistent state in case of errors, exceptions, and cancellations.

How do compensation events work in Camunda 8?

Saga

To enforce effective compensation, Camunda supports Saga, an interaction pattern that works well in data consistency management across complex microservice architectures. Unlike the two-phase commit protocol frequently used in distributed systems, the Saga pattern doesn’t reduce the overall system availability with transaction locks. It is also perfectly suited to modern NoSQL databases and message brokers that don’t support two-phase commit.

Thus, Saga is the preferable option for long-running business processes and highly distributed systems, which are almost universal in banking nowadays. It breaks complex processes into small, manageable steps, each of them accompanied by a compensation action needed for correct recovery in case of failure. As a result, the system remains fault-tolerant and consistent while retaining scalability and performance.

Zeebe and BPMN

Camunda’s Zeebe engine works as a Saga Coordinator responsible for either completing the overall transaction or aborting it, bringing the system into a known termination state. The engine has a log-based, event-driven design, where every state change is captured in an append-only log. This allows for efficient recovery and state management, which is crucial for implementing compensation events.

Compensation events in Camunda are created using the BPMN 2.0 standard in Camunda Modeler. They work closely with the engine, which handles process execution efficiently and can scale well.

When a process instance enters a compensation intermediate throw or end event, it triggers the compensation within its scope and invokes all compensation handlers of completed activities. The compensation handlers of active or terminated activities are not invoked. The compensation throw event remains active until all invoked compensation handlers are completed.

Specifics for the implementation of Camunda’s compensation events

• Camunda makes it easy to model compensation in complex business processes with visual BPMN representations.
• Users can create a compensation activity for every task in a process model, be it a service task, a human task, or a subprocess.
• Camunda’s compensation can handle complicated situations like multiple instance activities, compensation handler interruption, triggering compensation handlers in a given order, and more.
• Users don’t need to hand-code rollback logic across multiple microservices and can build robust error-handling mechanisms directly into their process models.

All in all, compensation flows built in Camunda save you the trouble of tracking each transaction individually and ease the complexity of rollbacks.

Real case of a compensation event for banking

Let’s look at how Camunda’s compensation works in a real-life banking scenario of transferring funds. As we’d like to focus on compensation events, we will slightly simplify the process by combining all client validations taking place before a fund transfer into one service task. The validation activities include:

• Checking the format and correctness of the data entered, such as IBANs and bank details
• Confirming that the specified amount does not exceed existing limits
• Verifying the sufficiency of funds in the sender’s account
• Identity verification
• Anti-fraud, anti-money laundering, and compliance checks

The diagram below shows the following scenarios:

1. If any problems are detected during the data validation, Camunda sends a notification informing the client about the fund transfer cancellation. The process is completed.
2. If the validation is successful, the process goes on.
3. In the next step, the sending bank writes off the specified amount from the client’s account. All the operations with the account are logged in the system.
4. If something goes wrong during the write-off (the connection was broken, the bank did not send a response about debiting the account, etc.), Camunda informs the client that the fund transfer process ends in an error. The process is completed.
5. If the write-off operation is successful, the process continues.
6. The next step is replenishing the receiver’s account with the specified amount.
7. If the system identifies an error boundary event during the execution of this operation, Camunda informs the client and triggers a compensation event—a rollback of writing off the funds from the client’s account. Then, the process is completed.
8. If the crediting causes no errors, Camunda logs the account replenishment operation and notifies the recipient of the funds added to their balance. The process is completed.

By implementing the compensation event, the bank ensures no funds are lost or incorrectly processed, maintaining data consistency and complying with regulatory requirements.

Compensation events: use cases and benefits for banking

Compensation events aren’t limited to simple debit-credit operations. They can be used across a wide range of banking processes:

• Loan application processing. An approved loan can be rolled back automatically when a post-approval check fails.
• Fraud detection. It is possible to reverse a flagged fraudulent transaction and notify the relevant departments in the process.
• Cross-border payments. In case of exchange rate mismatches or compliance violations, multiple currency conversions and international transfer operations can be efficiently handled with compensatory actions.
• Lending and mortgage transactions. If the credited party changes their mind or calculation errors are detected, it will be appropriate to change the agreement terms or roll the whole process back with compensation events.
• Commission and fee calculation. When a customer is mistakenly charged a commission or fee, a refund is a surefire way of correcting the situation.
• Settlements and clearing operations. In the event of failures in clearing processes (for example, in interbank transfers), compensation events can roll back and re-execute transactions or ensure the funds’ reversal.
• Payment systems and acquiring. When a payment system fails to process a payment, it can use compensation events to cancel the transaction and return the funds to the customer.
• Card transactions. Returning funds or cancelling the transaction will also be helpful in case of an erroneous debiting, for example, because of duplicate transactions.
• Currency transactions. If there is a change in the exchange rate during the conversion process, compensation events may adjust the original transaction or return funds with due regard to the new conditions.
• Deposit transactions. If a client terminates a deposit agreement early or there are errors in interest calculations, compensation events may adjust the maths or roll back the transactions.

By adopting automated compensation and letting Camunda coordinate complex banking processes, financial institutions can reap many benefits:

1. Minimise financial risk. By defining compensation flows, banks can reduce the risk of incomplete or erroneous transactions, ensuring that funds are accurately handled.
2. Ensure compliance. Automated rollbacks help prevent unauthorised or incomplete transactions and thus work toward meeting regulatory requirements like the PCI DSS and GDPR.
3. Reduce manual workload. Compensation events allow banks to eliminate manual intervention in case of transaction failures, freeing up valuable human resources for more high-value tasks.
4. Enhance customer trust. Banks can build greater customer trust by implementing accurate and transparent transaction processing.

Using compensation events beyond banking 
As event-based, distributed architectures are frequently found outside the financial industry, Camunda’s compensation solution has a much broader application area. Here are some examples of the compensation events usage in other industries:

• Supply chain management: Prevent stock discrepancies by reversing inventory changes if a shipment fails.
• Healthcare: Handle errors in patient data processing or treatment scheduling when managing clinical trials.
• E-commerce: Automatically cancel order confirmations and refund payments in case of errors in order processing or payment authorisation.
• Insurance: Ensure accurate financial records by cancelling overpayments or rectifying policy processing errors.

Final thoughts

By leveraging compensation events, organisations can build more resilient and adaptable process automation frameworks. Thanks to the faster recovery from failures and the reduced risk of process downtime ensured by automated compensation, businesses become more agile and competitive. Camunda 8 provides a powerful mechanism for banks to handle the complexity of modern financial transactions with confidence. By automating rollback actions, they can meet regulatory requirements and enhance operational efficiency while offering a more secure and reliable banking experience for their customers. Contact Camunda or a certified Camunda partner to get professional help in designing your compensation events.

Editor’s Note: This post was written by Maria Alish, a Camunda Champion. Would you like to become a Camunda Champion, and potentially have your blog post published here as well? Learn more about our Champion Program and how you can apply.

The post How a Bank Uses Compensation Events in Camunda 8 appeared first on Camunda.

It’s one thing to get a large language model (LLM) to generate a summary, write an email, or classify a support ticket. It’s another entirely to make sure that use of AI fits your company’s legal, ethical, operational, and technical policies. That’s where governance comes in—and frankly, it’s where most organizations are struggling to find their footing.

The challenge isn’t just technical. Sure, you need to worry about prompt injection attacks, hallucinations, and model drift. But you also need to think about compliance audits, cost control, human oversight, and the dreaded question from your CEO: “Can you explain why the AI made that decision?” These aren’t abstract concerns anymore—they’re real business risks that can derail AI initiatives faster than you can say “responsible deployment.”

That’s where Camunda comes into the picture. We’re not an AI governance platform in the abstract sense. We don’t decide your policies for you, and we’re not going to tell you whether your use case is ethical or compliant. But what we do provide is something absolutely essential: a controlled environment to integrate, orchestrate, and monitor AI usage inside your processes, complete with the guardrails and visibility that support enterprise-grade governance.

Think of it this way: if AI governance is about making sure your organization uses AI responsibly, then Camunda is the operational backbone that makes those policies actually enforceable in production systems. We’re the difference between having a beautiful AI ethics document sitting in a SharePoint folder somewhere and actually implementing those principles in your day-to-day business operations.

This post will explore how Camunda fits into the broader picture of AI governance, diving into specific features—from agent orchestration to prompt tracking—that help you operationalize your policies and build trustworthy, compliant automations.

What is AI governance, and where does Camunda fit?

Before we dive into the technical details, it’s worth stepping back and talking about what AI governance actually means. The term gets thrown around a lot, but in practice, it covers everything from high-level ethical principles to nitty-gritty technical controls.

We’re framing this discussion around the “AI Governance Framework” provided by ai-governance.eu, which defines a comprehensive model for responsible AI oversight in enterprise and public-sector settings. The framework covers organizational structures, procedural requirements, legal compliance, and technical implementations.

Camunda plays a vital role in many areas of governance, but none more so than the “Technical Controls (TeC)” category.. This is where the rubber meets the road—where your governance policies get translated into actual system behaviors. Technical controls include enforcing process-level constraints on AI use, ensuring explainability and traceability of AI decisions, supporting human oversight and fallback mechanisms, and monitoring inputs, outputs, and usage metrics across your entire AI ecosystem.

Here’s the crucial point: these technical controls don’t replace governance policies—they ensure that those policies are actually followed in production systems, rather than just existing as aspirational documents that nobody reads.

1. Fine-grained control over how AI is used

The first step to responsible AI isn’t choosing the right model or writing the perfect prompt—it’s being deliberate about when, where, and how AI is used in the first place. This sounds obvious, but many organizations end up with AI sprawl, where different teams spin up AI integrations without any coordinated approach to governance.

With Camunda, AI usage is modeled explicitly in BPMN (Business Process Model and Notation), which means every AI interaction is part of a documented, versioned, and auditable process flow.

You can design processes that use Service Tasks to call out to LLMs or other AI services, but only under specific conditions and with explicit input validation. User Tasks can involve human reviewers before or after an AI step, ensuring critical decisions always have human oversight. Decision Tables (DMN) can evaluate whether AI is actually needed based on specific inputs or context. Error events and boundary events capture and handle failed or ambiguous AI responses, building governance directly into your process logic.

Because the tasks executed by Camunda’s AI agents are defined with BPMN, those tasks can be deterministic workflows themselves, ensuring that, on a granular level, execution is still predictable.

This level of orchestration lets you inject AI into your business processes on your own terms, rather than letting the AI system dictate behavior. You’re not just calling an API and hoping for the best—you’re designing a controlled environment where AI operates within explicit boundaries.

Here’s a concrete example: if you’re processing insurance claims and want to use AI to classify them as high, medium, or low priority, you can insert a user task to verify all “high priority” classifications before they get routed to your fraud investigation team. You can also add decision logic that automatically escalates claims above a certain dollar amount, regardless of what the AI thinks. This way, you keep humans in the loop for critical decisions without slowing down routine processing.

2. Your models, your infrastructure, your rules

One of the most frequent concerns about enterprise AI adoption centers on data privacy and vendor risk. Many organizations have strict requirements that no customer data, internal business logic, or proprietary context can be sent to third-party APIs or cloud-hosted LLMs.

Camunda’s approach to agentic orchestration supports complete model flexibility without sacrificing governance capabilities. You can use OpenAI, Anthropic, Mistral, Hugging Face, or any provider you choose, and, starting with Camunda 8.8 (coming in October 2025), you can also route calls to self-hosted LLMs running on your own infrastructure. Whether you’re running LLaMA 3 on-premises, using Ollama for local development, or connecting to a private cloud deployment, Camunda treats all of these as different endpoints in your process orchestration.

There’s no “magic” behind our AI integration—we provide open, composable connectors and SDKs that integrate with standard AI frameworks like LangChain. You control the routing logic, prompt templates, authentication mechanisms, and access credentials. Most importantly, your data stays exactly where you want it.

For example, a financial services provider might route customer account inquiries to a cloud-hosted model, but keep transaction details and personal financial information on-premises. With Camunda, you can model this routing logic explicitly using decision tables to determine which endpoint to use based on content and context.

3. Design AI tasks with guardrails: Preventing prompt injection and hallucinations

Prompt injection isn’t just a theoretical attack—it’s a real risk that can have serious business consequences. Any time an AI model processes user-generated input, there’s potential for malicious content to manipulate the model’s behavior in unintended ways.

Camunda helps mitigate these risks by providing structured approaches to AI integration. All data can be validated and sanitized before it is used in a prompt, preventing raw input from reaching the models. Prompts are designed using FEEL (Friendly Enough Expression Language), allowing prompts to be flexible and dynamic. This centralized prompt design means prompts become part of your process documentation rather than buried in application code. Camunda’s powerful execution listeners can be utilized to analyze and sanitize the prompt before it is sent to the agent.

Decision tables provide another layer of protection by filtering or flagging suspicious content before it reaches the model. You can build rules that automatically escalate requests containing certain keywords or patterns to human review.

When you build AI tasks with Camunda’s orchestration engine, you create a clear separation between the “business logic” of your process and the “creative output” of the model. This separation makes it much easier to test different scenarios, trace unexpected behaviors, and implement corrective measures. Camunda’s AI Task Agent supports guardrails, such as limiting the number of iterations it can perform, or the maximum number of tokens per request to help control costs.

4. Monitoring and auditing AI activity

You can’t govern what you can’t see. This might sound obvious, but many organizations deploy AI systems with minimal visibility into how they’re actually being used in production.

Optimize gives you comprehensive visibility into AI usage across all your processes. You can track the number of AI calls made per process or task, token usage (and therefore associated costs), response times and failure rates, and confidence scores or output quality metrics when available from your models.

This monitoring data supports multiple governance objectives. For cost control, you can spot overuse patterns and identify inefficient prompt chains. For policy compliance, you can prove that AI steps were reviewed when required. For performance tuning, you can compare model outputs over time or across different vendors to optimize both cost and quality.

You can build custom dashboards that break down AI usage by business unit, region, or product line, making AI usage measurable, accountable, and auditable. When auditors ask about your AI governance, you can show them actual data rather than just policy documents.

5. Multi-agent systems, modeled with guardrails

The future of enterprise AI isn’t just about better individual models—it’s about creating systems where multiple AI agents work together to achieve complex business goals.

Camunda’s agentic orchestration lets you design and govern these complex systems with the same rigor you’d apply to any other business process. Each agent—whether AI, human expert, or traditional software—gets modeled as a task within a larger orchestration flow. The platform defines how agents collaborate, hand off work, escalate problems, and recover from failures.

You can design parallel agent workflows with explicit coordination logic, conditional execution paths based on agent outputs, and human involvement at any point where governance requires it. Composable confidence checks ensure work only proceeds when all agents meet minimum quality thresholds.

Here’s a concrete example: in a legal document review process, one AI agent extracts key clauses, another summarizes the document, and a human attorney provides final review. Camunda coordinates these steps, tracks outcomes, and escalates if confidence scores are low or agents disagree on their assessments.

6. Enabling explainability and traceability

One of the most challenging aspects of AI governance is explainability. When an AI system makes a decision that affects your business or customers, stakeholders want to understand how and why that decision was made—and this is often a legal requirement in regulated industries.

Modern AI models are probabilistic systems that don’t provide neat explanations for their outputs. But Camunda addresses this by creating comprehensive audit trails that capture the context and process around every AI interaction.

For every AI step, Camunda persists the inputs provided to the model, outputs generated, and all prompt metadata. Each interaction gets correlated with the exact process instance that triggered it, creating a clear chain of causation. Version control for models, prompts, and orchestration logic means you can trace any historical decision back to the exact system configuration that was in place when it was made.

Through REST APIs, event streams, and Optimize reports, you can answer complex questions about AI usage patterns and decision outcomes. When regulators ask about specific decisions, you can provide comprehensive answers about what data was used, what models were involved, what confidence levels were reported, and whether human review occurred.

Camunda as a cornerstone of process-level AI governance

AI governance is a team sport that requires coordination across multiple organizational functions. You need clear policies, compliance frameworks, technical implementation, and ongoing oversight. No single platform can address all requirements, nor should it try to.

What Camunda brings to this collaborative effort is operational enforcement of governance policies at the process level. We’re not here to define your ethics policies—we provide the technical infrastructure to ensure that whatever policies you establish actually get implemented and enforced in your production AI systems.

Camunda gives you fine-grained control over exactly how AI gets used in your business processes, complete flexibility in model and hosting choices, robust orchestration of human-in-the-loop processes, comprehensive monitoring and auditing capabilities, protection against AI-specific risks like prompt injection, and support for cost tracking and usage visibility.

You bring the policies, compliance frameworks, and business requirements—Camunda helps you enforce them at runtime, at scale, and with the visibility and control that enterprise governance demands.

If you’re looking for a way to govern AI at the process layer—to bridge the gap between governance policy and operational reality—Camunda offers the controls, insights, and flexibility you need to do it safely, confidently, and sustainably as your AI initiatives grow and evolve.

Learn more

Looking to get started today? Download our ultimate guide to AI-powered process orchestration and automation to discover how to start effectively implementing AI into your business processes quickly.

The post Ensuring Responsible AI at Scale: Camunda’s Role in Governance and Control appeared first on Camunda.

That’s what NatWest Bank, a leading retail and commercial bank in the UK, set out to do with the help of Camunda. Joanne Barry, Head of Technology Fraud Prevention COE at NatWest, led off her recent CamundaCon presentation by quoting the CEO of NatWest, Paul Thwaite, whose stated goal is to “build a simpler, more integrated and technology-driven bank that is capable of even greater impact.” One of the most powerful ways to do that in 2025 is by operationalizing AI.

A complex environment

Milesh Chudasama, Digital Transformation Director at NatWest, then took the stage to set the scene. The Fraud Center of Excellence group at NatWest includes over 800 fraud center agents using an average of 14 applications per call, with over 60 tools available to them across a range of platforms.

As you can imagine, this is a complex setup and it takes a long time to train people well to provide a good customer experience and to be in full compliance with regulations. And when they leave, all that painstaking training goes with them.

The lack of standardization

Inconsistency from a lack of standardization is a big problem. This not only means that customers are not served in the same way every call, but it also means that processes are often recreated across multiple platforms, duplicating effort.

Milesh explained that a lack of orchestration is holding them back, and that “what we want to do is create a good solid process orchestration engine.” This will enable a headless architecture that they can replicate across as many platforms as they need to, saving enormous amounts of time and effort.

Unlocking the value of AI for fraud detection

NatWest is looking for AI to drive specific business outcomes. Joanne explained, “We know AI will transform our ability to deliver” these key results:

1. Increase customer engagement
2. Maximize agility
3. Drive operating leverage

To drive those outcomes, they have outlined a series of initiatives where AI can produce valuable results. As Milesh put it, the goal is to “use AI in the right way, and not let it run free… and do some stuff that can damage your reputation.”

Analyzing and digesting large amounts of data, along with writing and testing code, were powerful ways that the team is able to use AI to speed up their work and reduce their time to market. AI can also build on existing rules-based systems for something like anomaly detection, helping them detect patterns quickly and be more proactive, rather than reactive.

One exciting initiative included using AI within Camunda to move “the creation of initial BPMN diagrams to the left, so that the business generates it themselves,” which will result in much tighter business-IT alignment and accelerate the way they build processes to begin with. AI will also help the team reduce the number of applications they need to touch to solve a problem, improving the way a process is “governed and managed by a human and an AI agent” or by straight-through processing.

Disrupt and transform

AI clearly has the power to both disrupt and transform banking and financial services. The team at NatWest has identified a number of opportunities.

• Customer experience: AI can respond faster and automate more cases, improving customer satisfaction.
• Engineering productivity: AI tools can help teams write code or design BPMN diagrams much more rapidly than before, speeding up production and making collaboration easier.
• Process simplification: Features like Optimize give them data points so they can understand what works and what doesn’t, and where AI can be most effective.
• Fraud/Financial crime: Understanding patterns and which things to react to and which not to, which is critical with the reports of fraud growing daily.

One key Milesh highlighted was the ability to determine “how far you want the AI to service a customer before you break out from the AI” and hand off to a human. This is a critical step in ensuring a good customer experience and avoiding the problems of AI attempting to navigate an issue that is too complex for it.

What’s next?

Milesh noted that what he really wants his team to do is make sure the customer gets the same journey no matter how they interact with the company. “Not omnichannel,” he observed, “let’s call it optichannel—the right customer to the right journey to get the right output.” Using tools like Optimize to simplify and automate processes, and orchestrating end-to-end with Camunda to better understand processes and remove human touchpoints where it’s most effective, will help them get there faster.

See the full presentation and more from CamundaCon Amsterdam 2025

You can check out the full presentation from NatWest here or watch it below, and for more be sure to check out all the recordings from CamundaCon Amsterdam 2025.

If you liked this and wish you’d seen it live, don’t miss your chance to join us at the next CamundaCon! Register now for CamundaCon New York 2025.

The post Reinventing Fraud Detection: NatWest’s Journey to Operationalize AI with Camunda appeared first on Camunda.

Camunda is designed to make these and many other challenges far easier when it comes to business process orchestration and automation, not to mention adding numerous features you may never have thought of. Let’s explore how a Camunda-based solution adds value to an organization, accelerates solution development, increases agility, and helps in reducing the TCO (total cost of ownership) and shortening the TTM (time to market) for companies using it over a homegrown solution.

Improved business-IT collaboration

With Camunda, business teams get end-to-end visibility and control over the business process flow. What you see is what runs in the system!

Compare the BPMN flow below with the same logic living in a verbose document or a piece of code—the visual graphical model speaks for itself in terms of intuitiveness and clarity. With Camunda, the same BPMN model is used to design, implement, operate the solution and create business user reports. This gives all stakeholders visibility into what logic exactly runs inside the system.

The need to keep the documentation updated as the project evolves is obsolete. The BPMN is live documentation as well.

Reduced development effort

Why reinvent the wheel? Camunda helps you reduce development effort and focus only on implementing your use case specific business logic.

BPMN provides out-of-the-box support for complex logic, like asynchronous waits on an incoming message, timer-based action triggers, clearly defined compensation activities to implement rollbacks in a distributed transaction, automated error handling with an option for human intervention, and open standards based business rules with DMN;. all of this with the powerful scalability that Camunda provides.

Programming all of these on your own is redundant when Camunda does this out of the box. Leverage Camunda accelerators like connectors and reusable assets like blueprints.

Calling a REST API, SOAP service, Lambda function, RPA system, SAP, CRM, Database, or any of the other cloud services? We have you covered! Use the out-of-the-box connectors and accelerate your efforts! Here’s a sample of how easy it is to make a REST call:

Leverage the greatness of AI without additional integration effort—use the AI connectors to bring in intelligent process flow execution with Camunda. AI becomes just another endpoint to be orchestrated rather than investing development effort to establish this integration.

Leverage our AI-based BPMN Copilot to convert your verbose use case documentation or existing programs with business logic to create BPMN. Save time and accelerate your implementation!

Create UI forms in a low-code fashion—make use of Camunda forms to quickly create UI forms using the drag-drop Camunda forms editor. Nontechnical business users can quickly create UI forms themselves, and for the cherry on top? You can create forms in Camunda and display them in your custom front end applications!

Gain end-to-end visibility across the process model

Camunda enables true end-to-end visibility across your whole process model. For example:

• Diagnostics and troubleshooting get easier since you can exactly pinpoint the status of the workflow or the point of failure without having to devour log files from multiple systems, especially in case of distributed systems or microservice architectures.
• This enhances the efficiency of the operations and support teams, which need fewer FTE, freeing up IT teams to focus on innovation.

Check out the screenshot below of a sample Operate dashboard demonstrating the data variables and point of failure.

Business reports and insights out of the box

With Optimize, Camunda helps you generate detailed reports automatically. We provide the ability for business users to create self-service reports that are near real time using a wizard-based approach without any query languages.

Use Optimize’s wizard-based approach to:

• Define your business KPIs and business audit reports
• Identify opportunities to improve the business process flow
• Create datasets that are ML-ready for further analysis
• Set up alerts in case of KPI accomplishments/SLA breaches and so on

Take a look at the sample Optimize dashboard below:

Increased operational agility

Changes to your business flows become much clearer and faster when you use Camunda. For example:

• Moving the business logic into BPMN and DMN cuts down on a lot of complexity, and maintenance over the longer term becomes easier.
• With traditional systems, you could need a daunting amount of time to analyze the impact of the change in the code base and implement the code changes while keeping the code base maintainable.
• With the business process depicted in the graphical BPMN notation, the impact analysis is short. Implementing the change could range from simply reorganizing the tasks in the BPMN to adding or removing tasks from the BPMN to modifying your service-task or custom connector implementations to changing the rules in DMN tables. All much easier and more straightforward.

Agile and IT teams can quickly respond to change requirements to meet business needs, ensuring that your solution can remain compliant to ever-changing regulatory compliance requirements. This of course also enables your business to remain competitive in the market.

Disaster Recovery

Zeebe has built-in active-active replication, which means you have Disaster Recovery (DR) out of the box. This lays the foundation for a resilient and highly available stage engine. Additionally we also support multi-region setups, enabling active-active or active-passive modes of Disaster Recovery across different geographical regions to ensure resilience for mission critical applications.

Innovate at any scale

Since there is no database backing its state, Camunda can scale almost linearly to meet your growing throughput requirements as your business grows! Need more throughput? It could be as simple as adding more nodes and scaling the Zeebe Cluster.

Our benchmarks have demonstrated that we can handle hundreds of process instances and thousands of task instances per second. Camunda can also handle both high throughput and low-latency use cases. Take a look at the following Grafana snapshot from a Camunda benchmark run using a BPMN process with 11 service tasks.

An open, composable, and flexible architecture

We use open standards for building the process models and implementing business rules. Camunda strives to provide low-code features to accelerate solutioning while also being developer-friendly to implement customised solutions.

By now, we all know that customisations to out-of-the-box features are often necessary. Camunda is flexible and composable, allowing you to replace out-of-the-box components with custom coding where needed. Design standardized solutions without vendor lock-in!

We’ll take care of process orchestration so you can focus on your business problems

Rather than building all these capabilities from scratch again, leverage our orchestration platform’s capabilities to help your IT teams focus on just solving your business problems. We’ll take care of orchestrating your business process flows end-to-end at scale!

The rest of the iceberg

What’s next? Camunda’s flexible, scalable, and intelligent orchestration and automation platform empowers you to create a composable and best-of-breed architecture. Operationalize AI to meet your current business requirements while staying comfortably poised to leverage other new technological disruptors as they become available in the future.

The platform provides a number of out-of-the-box components like RPA to integrate with legacy systems, Tasklist to enable human interaction, a DMN-based business rules engine, and Intelligent Document Processing.

Camunda is also flexible enough to be used in a plug-and-play fashion. Swap a certain OTB component with your specific solution. For instance, you could swap Camunda’s RPA with your existing RPA solution, or swap the Tasklist with your custom UI.

Similar to a building made of LEGO blocks, Camunda provides the flexibility to swap out the components that you want with other implementations to suit your needs. This lets you eliminate vendor lock-in and discover more value for your business!

Moving from a legacy solution to Camunda

Hopefully the value Camunda provides is easy to see, but of course it’s still a change if you’re already using something else. If you’re evaluating the effort to migrate from your programming-based solution to Camunda, be sure to:

• Check out Camunda’s BPMN Copilot to convert the business process logic in your programming code like Python, Java, C# etc into BPMN quickly. This saves a huge chunk of time while migrating your business process flow to Camunda.
• Consider that you can refactor business-specific logic into service tasks or project/enterprise-specific connectors, promoting standardisation and reuse.
• Talk to us! We’re happy to explore the possibilities and benefits for your specific use case!

The post The Benefits of Using Camunda Compared to Traditional IT Solutions appeared first on Camunda.

Most agent builders will wrap up those three requirements into a single, static, synchronous system, but at Camunda we decided not to do this. We found that it creates too many use case limitations, it’s not scalable and it’s hard to maintain. To overcome these limitations, we came up with a concept that lets us decouple these requirements and completely visualize an agent in a way that opens it up to far more use cases, not only on a technical level, but also in a way that  alleviates a lot of the fears that people have when adding AI agents as part of their core processes.

The value of a complete visualization

Getting insight into how an AI Agent has performed in a given task often requires someone to read through its chain of thought (this is like the AI’s private journal, where it details how it’s thinking about the problem). This will usually let you know what tools it decided to use and why. So in theory if you wanted to check on how your AI Agent was performing, you could read through it. In practice, this is just not practical for two reasons:
1. It limits the visibility of what happened to a text file that needs to be interpreted.
2. AI agents can sometimes lie in their chain of thought—so it might not even be accurate.

Our solution to this is to completely visualize the agent, its tools and its execution all in one place.

Gain full visibility into AI agent performance with BPMN

The diagram above shows a BPMN process that has implemented an AI agent. It’s in two distinct parts. The agent logic is contained within the AI Task Agent activity and the tools it has access to is displayed with an ad-hoc sub-process. This is a BPMN construct that allows for completely dynamic execution of the tasks within it.

With this approach the action of an agent is completely visible to the user in design time, during execution, and can even be used to evaluate how well the process performs with the addition of an agent.

The diagram above shows a headmap which shows which tools take the longest to run. This is something impossible to accurately measure with a more traditional AI agent building approach.

Decoupling tools from agent logic

This design completely decouples the agent logic from the available tool set. Meaning that the agent will find out only in runtime what tools are at its disposal. The ramifications of this are actually quite profound. It means that you can run multiple versions of the same process with the same agent, but a completely different tool set. This makes context reversing far easier and also lets us qualitatively evaluate the impact of adding or removing certain tools through AB testing.

Improving maintainability for your AI agents

The biggest impact of this decoupling in my opinion though is how it improves maintainability. Designers of the process can add or remove new tools without ever needing to change or update the AI agent. This is a fantastic way of separating responsibilities when a new process is being built. While AI experts can focus on ensuring the AI Task Agent is properly configured, developers can build the tooling independently. And of course, you can also just add pre-built tools for the agent to use.

Composable design

Choosing, as we did, to marry AI agent design with BPMN design means we’ve unlocked access for AI agent designers to all the BPMN patterns, best practices and functionality that Camunda has been building over the last 10 years or so. While there’s a lot you gain because of that, I want to focus on just one here: Composable architecture.

Composable orchestration is the key to operationalizing AI

Camunda is designed to be an end-to-end orchestrator to a diverse set of tools, rules, services and people. This means we have designed our engine and the tools around it so that there is no limitation on what can be integrated. It also means we want users to be able to switch out services and systems over time, as they become legacy or a better alternative is found.

This should be of particular interest to a developer of AI agents because it lets you not only switch out the tools the AI Agent has access to, but more importantly, it lets you switch out the agent’s own LLM for the latest and greatest. So to add or even just test out the behaviour of a new LLM no longer means building a new agent from scratch—just swap out the brain and keep the rest. This alone is going to lead to incredibly fast improvements and deployments to your agents, and help you make sure that a change is a meaningful and measurable one.

Conclusion

Building AI agents the default way that other tools offer right now leads you to adding a new black box to your system. One that is less maintainable and and far more opaque in execution than anything else you’ve ever integrated. This is going to make it hard to properly maintain and evaluate.

At Camunda we have managed to open up that black box in a way that integrates it directly into your processes as a first-class citizen. Your agent will immediately benefit from everything that BPMN does and become something that can grow with your process.

It’s important to understand that you’re still adding a completely dynamic aspect to your process, but this way you mitigate most concerns early on. For all these reasons, I can imagine that of all the many, many AI agents that are going to be built this year, I’m sure the only ones that will still be used by the end of next year will be built in Camunda with BPMN.

Try it out

All of this is available for you to try out in Camunda today. Learn more about how Camunda approaches agentic orchestration and get started now with a free trial here.

The post The Benefits of BPMN AI Agents appeared first on Camunda.

As developers explore how to make multi-agent systems useful and reliable, new communication standards are emerging to address the need for interoperability, security, and shared understanding. Three notable efforts in this domain are:

• Model Context Protocol (MCP) developed by Anthropic
• Agent Communication Protocol (ACP) developed by IBM Research
• Agent2Agent (A2A) Protocol developed by Google and Microsoft

Each targets a specific layer of the multi-agent interaction stack and reflects different philosophical and architectural priorities.

Model Context Protocol (MCP)

Anthropic developed the Model Context Protocol (MCP) to solve a narrow but critical problem: how to give large language models (LLMs) structured context about tools, APIs, and systems they can interact with. MCP focuses on standardizing the input context that LLMs receive before agents execute their tasks. As the Anthropic documentation says, “Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.”

With MCP, tools expose a structured schema such as an OpenAPI or JSON schema along with natural language descriptions. The LLM receives the schema via MCP when it is being prompted to act, ensuring a consistent understanding of available actions.

As an early example of agent-to-agent communication, MCP has several advantages. It has promise as a way to equip AI agents with structured context to call APIs, tools, and plugins intelligently. It’s an open specification that’s designed to work with any LLM or agentic framework, making it highly flexible. And it’s lightweight and easy for software developers to adopt because it aligns with common software development practices.

However, it’s important to note that MCP is limited to tool and model interactions; it’s not a general-purpose agent protocol. As of now, it doesn’t define inter-agent negotiation or dynamic delegation of tasks.

Example: Customer onboarding in financial services

Imagine a customer onboarding process at a retail bank, which requires validating identity documents, performing Know Your Customer (KYC) checks, interfacing with fraud detection services, and activating new customer accounts. Traditionally, each of these tasks is handled by a separate back-end service with its own API.

Using MCP, an AI-powered onboarding agent could be equipped with structured, real-time context about all of these APIs. MCP ensures the agent understands what each service does, how to call it, and what inputs and outputs to expect, without the need for a developer to hard-code specific logic into the model.

This allows the onboarding agent to dynamically compose API calls, intelligently route requests, and adapt its workflow if a service is temporarily unavailable—all while minimizing human intervention. The result is a faster, more consistent onboarding experience that reduces manual handoffs and operational delays.

Agent Communication Protocol (ACP)

The Agent Communication Protocol (ACP) developed by IBM Research is designed to define how autonomous AI agents communicate with one another, with an emphasis on structured dialogue and coordination across heterogeneous systems. It aims to provide a shared semantic foundation for multi-agent communication, including message types, intents, context markers, and response expectations.

With ACP, agents exchange structured messages that encapsulate intention, task parameters, and context. The protocol enables dynamic negotiation between agents—for example, for delegation or task refinement.

ACP’s strong focus on semantics and interoperability mean it has the potential to become a very powerful protocol for high-level coordination of agents (beyond simple messaging). It can facilitate distributed task-solving by autonomous agents that have overlapping goals.

However, a potential hurdle to adoption is that ACP requires agent developers to agree on shared ontologies. This may position it as the protocol of choice for development teams that work for the same company or that work on the same software product. ACP is still in the early stages of development in terms of syntax, implementation, and tooling, so much remains to be seen as it grows.

Example: Supply chain coordination across departments

Consider a global manufacturer with autonomous agents representing procurement, inventory, and logistics. These agents must coordinate continuously to maintain optimal stock levels, anticipate shortages, and reroute shipments when needed. Using ACP, these agents could engage in structured, semantically rich dialogue to negotiate changes in supply orders, reallocate inventory based on real-time demand forecasts, or trigger alerts if a delay will cause cascading disruptions. For example:

• The procurement agent might notify logistics: “Delay expected from supplier X. Can we reassign delivery Y?”
• The logistics agent can respond: “Yes, rerouting via warehouse Z. Updating inventory accordingly.”

By using shared ontologies and structured message types, ACP aims to support adaptive, high-fidelity inter-agent collaboration across teams and systems. This is especially valuable in environments where distributed decision-making and resilience are key.

Agent2Agent Protocol (A2A)

The Agent2Agent Protocol (A2A) being developed by Google with support from Microsoft is another open standard. It’s designed to allow different AI agents from different companies or domains to exchange messages and perform coordinated tasks. Its development was prompted by the growing use of LLM-based agents in workflows that span multiple applications.

In A2A, agents advertise their capabilities using a structured metadata format called “agent cards.” Agents then communicate through signed, structured messages based on a shared schema. A2A includes provisions for trust, routing, and structured memory exchange. This design maximizes the options for composability and cross-platform collaboration between AI agents.

While A2A is a community-driven project, it is supported by two of the largest software companies in the world, both of which are cloud providers and LLM vendors, which may accelerate its development. However, the protocol is still an early-stage alpha with evolving security and governance capabilities, making it difficult for other vendors to start developing with it.

Example: Cross-platform customer support automation

Picture a scenario where a retail company uses Google Workspace, Zendesk, Salesforce, and Microsoft Teams. Different LLM-based agents exist in each environment and perform tasks such as summarizing conversations, logging support tickets, updating customer relationship management (CRM) records, and scheduling follow-ups.

With A2A, these agents can collaborate across platforms:

• A Google-based agent summarizes a support call and shares the summary with a Salesforce agent
• The Salesforce agent updates the customer record and flags a follow-up
• A Microsoft-based assistant sees the flag and books a Teams meeting with the customer

Through agent cards and structured messaging, A2A aims to enable interoperability across agent ecosystems, so tasks can flow fluidly without brittle, point-to-point integrations. This supports consistent, personalized, and efficient customer service—at scale.

Comparing developing communication protocols

The following table summarizes the current state of MCP, ACP, and A2A:

As you can see, these three protocols represent complementary approaches to the problem of inter-agent communication. MCP addresses the immediate need to contextualize LLMs effectively; ACP looks further ahead at semantic richness and intent modeling; and A2A targets broad interoperability across agents and platforms.

For developers and organizations building agentic architectures, understanding and experimenting with these protocols will be essential. While no single standard has emerged as dominant, the collective momentum suggests that interoperability and shared context will be key to unlock the full potential of multi-agent AI.

Camunda can help you operationalize AI agents

Whether you’re new to agentic AI or you’ve already started building agents, Camunda process orchestration and automation can help you put AI into action. To learn about Camunda’s agentic orchestration capabilities, check out our guide, “Why agentic process orchestration belongs in your automation strategy.”

The post MCP, ACP, and A2A, Oh My! The Growing World of Inter-agent Communication appeared first on Camunda.

We’ve seen time and time again that task-based automation might deliver quick wins, but it doesn’t scale. The moment processes get complex, those isolated tools start pulling in different directions. The result? Broken customer experiences. Inefficient teams. A lack of visibility and an inability to improve processes.

That’s the problem we set out to solve back in 2013. And it’s the same problem we continue to solve—only now, the stakes are higher.

AI is changing everything. Nearly every conversation we’re having with customers right now touches on it. According to the 2025 State of Process Orchestration and Automation Report, 84% of organizations want to add more AI capabilities over the next three years. But 85% struggle to make AI actually work at scale.

There are a few reasons why this is happening. First, simply adding AI into an automation strategy doesn’t magically create value. Done incorrectly, it just creates another silo—and yet another layer of technical debt. 

Second, traditional process automation focuses on automating around a set of predetermined rules (or deterministic orchestration). AI presents the opportunity to break those rules by executing processes dynamically.

That’s where agentic orchestration comes in.

Overcoming limitations in traditional process design

Process orchestration as we know it is deterministic, meaning you design processes and define their logic in advance. Sure, it can handle variants, but only if they’re a part of the original process model in BPMN or DMN. What we think of today as a fully automated process, or “straight through processing” (STP), usually relies on this structure.

AI agents make process automation much more dynamic. Dynamic orchestration uses AI to handle “unforeseen” tasks. It orchestrates based on defined goals and a given context, but doesn’t need specific instructions like a deterministic process.

But most business processes are somewhere in the middle. They have some STP in place, but are still using human case management to handle exceptions or tasks without a straightforward action. Agentic orchestration blends deterministic and dynamic orchestration seamlessly.

For example, most of the time, STP is done in seconds or minutes. But sometimes it fails. And when it does, people step in to investigate. It’s slow, messy, and manual. That’s where AI can help. Agentic orchestration takes over when the unexpected happens—analyzing unstructured data, spotting patterns, and suggesting actions.

Real world examples of agentic orchestration

And here’s where things get really exciting: This isn’t theoretical anymore. It’s real. It’s working. And it’s already creating serious value.

Our partner EY has built a tool for agentic trade reconciliation with Camunda. Reconciliation errors are usually handled manually. Because they are very labor intensive, they take a lot of time to review and are error-prone—resulting in a risk of fines. In fact, the world’s largest banks employ up to 25,000 people to review these exceptions. With agentic orchestration, they’re now using AI to suggest the next best action based on trade data and LLMs. That means faster resolution and T+1 compliance. But the most impressive value is in productivity: With agentic trade reconciliation, one employee can now handle far more cases per day on average, resulting in an increase in productivity of 7x.

Here’s another example: Payter, a payment terminal business for vending machines, is drowning in case management when payments fail. They have now started using Camunda to blend deterministic process logic with AI agent-driven exception handling. The expected outcome? Resolution times will drop by 50% from 24 to 12 minutes. Even better? Customer service will improve not just because of the shorter resolution time, but also because employees are now able to spend more time on complex issues.

Building the autonomous organization of the future

And the examples above are only the beginning. We’re seeing more and more companies wanting to bring more AI into their processes. In order to do that, they’re operationalizing AI in a way that’s composable, scalable, and flexible—not stuck in isolated systems. And Camunda is at the foundation of this shift. We’ve spent over a decade building a platform that does one thing exceptionally well: orchestrate complex, mission-critical processes from end to end.

Now, we’ve taken our powerful orchestration engine and infused it with embedded AI. The result? The ability to blend deterministic and dynamic orchestration in a unified agentic orchestration model—with guardrails, auditability, and control.

What does that mean in practice?

It means you can now:

• Blend structured BPMN and DMN process modeling with flexible AI agents.
• Automate what was once “un-automatable” (like complex case management).
• Inject AI into your legacy systems without a big bang transformation.
• Use low-code tools and connectors to move fast.
• Implement AI safely and reliably, with “guardrails” for full auditability and control.

We’re giving you AI-native capabilities, like:

• Ad-hoc sub-processes: Let agents decide what happens next.
• Camunda Copilot: Go from a text prompt to a running process.
• RPA and IDP: Integrated, out-of-the-box, and ready to go.
• ERP Integration: Orchestrate AI across SAP, ServiceNow and beyond.

Here’s a look into the future: AI agents that get even smarter by working alongside humans—automating more and more over time. Think AI loan specialists that are trained directly from human input.

Our long-term vision hasn’t changed

We’ve always believed in end-to-end process orchestration. What’s different now is how far we can take it. Agentic orchestration brings us closer to a world where AI and humans truly collaborate across systems, teams, and time zones. We’re designing the autonomous, AI-powered organization of the future.

If you’re thinking about bringing agents into your business—this is the moment. With Camunda, you’ve got the foundational technology and the vision to do it right.

The next chapter of automation just started. And I couldn’t be more excited.

Let’s build the future together.

Learn more

You can learn more about our agentic orchestration capabilities here, and if you want to dive deeper, be sure to watch the recording of the keynote from CamundaCon 2025 Amsterdam (available soon).

The post Agentic Orchestration: Automation’s Next Big Shift appeared first on Camunda.

Sound familiar?

Your customers now choose financial providers like items from a menu, selecting specialized services rather than committing to a single institution. This fragmentation reflects a fundamental shift in banking, driven by two converging forces: the largest wealth transfer in history and the uncompromising demand for tailored services and products.

Trust me when I say, I don’t want the complexity of multiple banks and apps. But if I can’t get what I need, I will find it elsewhere.

The $124 trillion opportunity banks can’t afford to miss

Right now, $124 trillion is beginning to shift from older generations to younger ones. This transfer will happen over just 23 years, not in some distant future. For banks, this isn’t just money changing accounts. This represents a fundamental transformation in how wealth flows across generations and accounts.

Look at the warning signs. According to the Williams Group, 90% of family wealth disappears by the third generation. Most heirs feel unprepared. Only 22% of parents inform their children about inheritance plans before age 25. This knowledge gap creates vulnerability and opportunity.

Your younger customers invest differently. They prioritize impact investing, ESG considerations, and philanthropy. Two-thirds of customers under 40 allocate 70% of their investments to ESG opportunities. They demand different tools, different approaches, and different conversations about money.

Your customers won’t wait

Remember when waiting 2-3 days to move some money around was acceptable? Now, even 2-3 hours feels like an eternity. And given the market volatility we’ve seen lately, even a few hours can mean the difference between a good financial choice and a bad one.

The Financial Conduct Authority discovered that 66% of younger investors spend less than 24 hours deciding on investments. A shocking 14% make these decisions in under an hour. This isn’t just impatience. This is a new operational reality.

Your customers expect to move funds instantly between checking, savings, investment accounts, and retirement funds. They want to monitor positions across distributed accounts. They need the ability to move money at precisely the right moment to seize opportunities in increasingly volatile markets.

And they want to do so with ease. Ironically enough, this is also a key desire for wealth advisors serving these customers: less context switching, more customer focus. A case in point is during significant life events.

Gartner research showed that approximately 50% of younger generations prefer to visit an agent at a bank branch or speak to someone on the phone for major events compared to digital-only channels. The demand for blended experiences that combine digital speed with human expertise has never been higher.

Financial wellbeing starts with knowing your customer

With 48% of consumers feeling financially worse off than last year, your institution has a profound opportunity to deliver meaningful value through holistic wellbeing services.

But here’s the hard truth. Only 40% of financial services companies are confident their customer profiles are both complete and accurate. With today’s technological capabilities, how can there be such a wide gap? Without foundational data, personalized guidance becomes impossible.

Think about what your customers actually experience. Most have fragmented financial identities spread across multiple institutions. They receive disconnected advice based on partial information. They struggle to make confident decisions amid complexity and uncertainty.

Even if your customers are loyal and stick with your bank, the likelihood that your customer data is complete is low. The rise of Open Banking and Open Finance initiatives promises to solve this problem, but most institutions still struggle to align strategy and execution.

What customers truly want is reassurance from trusted experts who see their complete financial picture and can provide guidance based on that holistic view.

Three imperatives for surviving the great wealth transfer

To thrive in this new landscape, focus on three practical initiatives.

Connect everything

Create an ecosystem that bridges all endpoints, from modern APIs to legacy systems and third-party providers. In the Open Banking era, the banks seeing 30% higher customer satisfaction are those enabling seamless data sharing across institutional boundaries.

Your customer doesn’t care about your technical debt. They care about complete financial visibility that enables better decision-making and customer experiences. Process orchestration turns fragmented systems into cohesive customer journeys.

Apply the right level of governance

As AI-driven advisory services expand, establish governance that maintains trust without sacrificing speed. Financial institutions that operationalize AI in one central platform reduce compliance risks while accelerating service delivery.

This isn’t just about risk mitigation. It’s about turning regulatory requirements into competitive advantages through consistent, auditable processes.

Stay agile

The financial landscape changes constantly. Implement operations that enable continuous adaptation to changing market conditions. Banks that build standardized, reusable process components launch new products with 45% less effort.

Your technology must evolve as rapidly as customer needs, with modular components that can be reconfigured without disrupting existing services.

Finding the human balance

The ultimate challenge isn’t technological. It’s human.

Your customers need proper guardrails alongside instantaneous advisory services. They need the freedom to move quickly alongside the wisdom to think long-term. They need digital convenience alongside human expertise.

The most successful institutions will recognize that customers increasingly choose specific providers for specific purposes. They’ll open their ecosystems to gather information across institutional—and even industry—boundaries, creating complete customer profiles that enable truly holistic advice.

Your success won’t come from simply moving money faster or cheaper.

It will come from orchestrating processes that help customers make better financial decisions in real time, transforming the greatest wealth transfer in history into the greatest opportunity for financial wellbeing we’ve ever seen.

For more on this topic, you can watch Sathya Sethuraman, field CTO at Camunda, and Shane Ernest, senior product marketing manager at Camunda, discussing the issue in the video below.

This article is the third in our Financial Services Reflection Series, examining how 2025’s banking predictions are playing out in reality. Read Sathya Sethuraman’s original “2025 Banking & Financial Services Predictions” and explore our solutions for building adaptable banking operations.

The post Money in Motion: A $124 Trillion Moment of Truth appeared first on Camunda.

They can execute complex scams in minutes using AI. And with real-time payments, they can wire money across different accounts using synthetic identities. Meanwhile, legitimate cross-border transfers can still take weeks at certain institutions. This gap represents both a challenge and an opportunity for banks ready to transform their approach to commercial services.

In a recent webinar, Enrico Camerinelli, strategic advisor at Datos Insights, and Sathya Sethuraman, field CTO for banking and financial services at Camunda, explored how process orchestration and automation enables banks to bridge this divide and generate more value for commercial banking clients.

What commercial clients really want

As Camerinelli explains in the webinar, Datos Insights research reveals that nearly 90% of corporate treasurers consider it essential to run banking operations directly from their enterprise systems. This seamless integration creates significant orchestration challenges spanning technology, processes, and people. Not just for the bank itself, but also for commercial clients.

“Corporate users want to control inbound and outbound transactions directly from their enterprise system,” explains Camerinelli. “But this integration creates potential break points that require thoughtful orchestration of applications on both the enterprise and banking sides.” Half of corporate treasurers cited issues with integration, multiple screens, and a high dependence on Excel or external systems.

Without effective orchestration, this integration challenge creates risk. When Datos Insights asked why corporate treasurers partner with fintech firms instead of traditional banks, they cited better functionality (48%), more payment options (46%), better integration with internal systems (43%), and access to real-time payments (41%).

These proof points show the growing risk posed to incumbent banks that are slow to respond to their clients’ needs. If commercial clients don’t get what they need, they’ll pursue other options without thinking about loyalty or long-standing relationships.

Why API catalogs alone fail to deliver value

Many banks have responded to integration demands by building extensive API catalogs, but this approach creates new problems rather than solutions.

“The more API catalogs banks create, the more they risk widening the divide from corporate users,” notes Camerinelli. “These APIs exist, but corporate clients struggle to use them efficiently or build ROI from them.”

According to Datos Insights data, the challenges are multithreaded. The biggest hurdle is the underlying process and operational changes are difficult to manage, with over 50% citing this obstacle. Cost (45%) and IT dependence (41%) issues were cited next, which is expected given the complexity of a modern enterprise.

Without a strategic framework in place, banks can often find their APIs and technology stacks grow out of control. As teams can often work in silos, they risk building the same integration multiple times. The lack of reuse and increased duplication isn’t just bad for productivity. It increases maintenance costs and adds to the risk of complexity, which ultimately increases the total cost of ownership. 

Instead, banks need to think differently. They need to think beyond APIs and offer customers what they want.

Focusing on API calls is missing the point

When discussing open banking, conversations should fixate on the end users. However, research shows that corporate clients have a matrix of important capabilities. They prioritize liquidity, convenience, security, and of course, yield. But each of these elements carry different weights.

“Corporate treasurers want yield, but not at the cost of missing other priorities like sufficient liquidity, safety, and ease of use,” explains Camerinelli. “No treasurer will lose their job for missing a few extra basis points, but they certainly will if they compromise liquidity or security.”

Sethuraman adds that properly orchestrated open banking actually enhances security while delivering on these other priorities: “By opening your APIs strategically, you can embed your services into corporate ERPs. This delivers the functionality and capabilities they demand.”

Orchestrating and scaling AI capabilities in banking

Process orchestration creates the critical framework for effectively applying artificial intelligence across banking operations. This represents a shift from isolated AI applications to a cohesive approach for embedding capabilities spanning deterministic and non-deterministic processes. 

Yet in banking, there needs to be a balance of both. Mission-critical processes need to function as designed. Every time. Otherwise, there’s a risk of disruption, regulatory action, or brand impact.

Agentic AI opportunities are still plentiful in the industry that’s practically led the revolution. Having the ability to blend both gives banks the freedom to apply the right technology at the right time instead of being limited.

Orchestration allows banks to speed up deploying new models or capabilities. It prevents AI hallucination risks while creating a governance framework that helps banks accelerate innovation without compromising safety.

For example, one bank monitoring for synthetic identity fraud implemented an agentic approach that allowed their fraud team to identify repetitive patterns in certain document types without disrupting their existing processes. They could test these patterns with real data, refine their models, and gradually deploy improvements. 

By essentially A/B testing fraud models, the bank was able to reduce false positives while simultaneously improving the detection of bad actors. Something impossible with traditional, static approaches.

Building incrementally while maintaining vision

One of the most powerful advantages of process orchestration is enabling incremental modernization within a coherent strategic framework. Rather than waiting years for comprehensive implementations, banks can deliver value continuously from the start.

Sethuraman described how one multinational bank evolved from a narrow payment system implementation in one country to a 70-plus country platform vision through orchestration. Without process orchestration, they would have faced an impossible choice: wait years for a complete solution or implement disconnected point solutions.

“Process orchestration provides the flexibility to start small but think big,” he explained. “The bank didn’t wait five years to deliver value. They incrementally built their platform while maintaining a consistent vision.”

This approach requires business and IT collaboration to map the true vision, identify customer requirements, and build, buy, and blend what’s needed to achieve strategic goals. Process landscapes allow business stakeholders to create standardized process hierarchies and catalogs that IT can implement incrementally, preventing both analysis paralysis and technology sprawl.

When another audience member asked if orchestration just adds more complexity to overlapping systems, Camerinelli clarified: “Orchestration isn’t just connecting fragmented pieces. It ensures processes are properly reviewed and revised first. You’re not just automating existing problems. You’re resolving them within a coherent framework.”

The path forward

As open banking transforms commercial relationships, process orchestration provides the critical link between everything and enables rapid innovation.

When implemented thoughtfully, you can:

• Create secure connections to client systems with appropriate permissions
• Deliver enhanced functionality that meets rising expectations
• Apply AI intelligently to improve experiences while reducing costs
• Build incrementally toward a comprehensive, bank-wide transformation that reduces the total cost of ownership and enables faster scaling

Process orchestration transforms open banking from a checkbox exercise into a growth opportunity. It balances innovation with practical security measures that protect you and your clients while delivering the capabilities commercial clients actually value.

Ready to learn how process orchestration helps banks grow revenue with open banking? Watch the complete conversation between industry experts Enrico Camarinelli and Sathya Sethuraman to discover practical strategies for balancing innovation with security in open banking. 

The post How to Grow Commercial Revenue with Open Banking appeared first on Camunda.

Taking a closer look, you can see how much process orchestration is intertwined with improved customer experience. Exceptional customer service relies on well-orchestrated processes; without them, any success is often due to luck and requires considerable effort to sustain or repeat.

A study by Forrester, The Total Economic Impact of Camunda, highlighted that adopting a process orchestration solution like Camunda could significantly help organizations deliver value to customers faster. Organizations that can scale to handle more customer interactions can improve long-term customer experience.

According to the head of IT at one insurance company that implemented Camunda:

“The customer experience increased dramatically. You cannot measure that in euros or dollars, but it has an impact that management sees.”

Why is outstanding customer service so important?

Let’s explore a bit more as to why organizations need to invest in providing exceptional customer experiences. We’ll also take a look at how process orchestration improves CX through streamlined workflows, integrated systems, real-time data, reduced bottlenecks, and increased flexibility.

Customers expect more than ever from companies.

Today’s customers demand quick, flawless services without delays or mistakes that could cause frustration. Additionally, there is a growing expectation for highly personalized experiences.

According to a Zendesk report about CX trends:

• 72% of customers want immediate service.
• 60% of consumers have purchased something from one brand over another based on the service they expect to receive.
• 64% of customers will spend more if a business resolves their issues where they already are.

Investment in CX is critical to bottom-line growth

Forbes states that acquiring a new customer can cost five to seven times more than retaining an existing one. This significant cost difference underscores the importance of customer retention for sustainable growth. Retaining existing customers not only saves money but also leverages the established relationship and trust, leading to higher lifetime value.

Yet, despite investments in the latest technologies, many organizations still offer broken or inefficient customer experiences, driving away even the most loyal customers after just two negative interactions. This highlights the critical need for businesses to ensure seamless and efficient customer journeys.

Rapid services and problem resolution not only improve customer satisfaction but also strengthen trust and loyalty. When customers feel heard and valued, they are more likely to continue their relationship with the brand. Quick and effective problem-solving approaches can turn potentially negative experiences into positive ones, further solidifying customer loyalty and contributing to long-term business success.

Growing competition from digital natives

The rise of digital-first startups and other disruptors has intensified competition across all industries. Companies now face immense pressure to attract and retain customers, making it essential to adopt an omnichannel approach, new technologies, and embrace digital transformation.

To keep up with the competition, they need to integrate customer journeys and transform fragmented business processes into cohesive, seamless experiences. Without such investments, companies risk falling behind and losing their customer base to more agile competitors.

How process orchestration contributes to improved customer experience

Process orchestration integrates various automated and manual tasks, and can connect multiple workflows to create a seamless end-to-end process.

Three ways process orchestration improves customer service

This approach contributes to a significantly better customer experience in three important ways:

1. It enhances visibility into customer journeys, revealing new optimization opportunities and fostering collaboration between business and IT teams. This eliminates siloed, error-prone automation by introducing a unified model.
2. It ensures consistent execution of customer processes at scale, reducing errors, preventing bottlenecks, and maintaining high-quality experiences across all channels.
3. It offers flexibility by allowing companies to quickly adapt to changing requirements without disrupting the entire process. This flexibility supports the use of preferred technologies and tools, avoiding vendor lock-in.

Moreover, the reusability of modeled processes lowers the total cost of ownership and speeds up time to market for both customer experiences and other business processes within the organization.

Let’s break down what this looks like in practice. What can be achieved with process orchestration and how does it impact the customer experience?

Streamlined workflows lead to faster and more reliable services

By ensuring tasks are completed in the correct sequence, process orchestration significantly boosts efficiency and accuracy across the entire organization. Automated task assignments direct work to the right team members based on their skills and availability, leading to faster and more reliable customer service.

Companies can also use automation via flexible business rules, such as Decision Model and Notation (DMN), to reduce human bottlenecks in decision-making. With DMN, they can define rules in a clear and structured way, so processes can move forward without needing manual approval or input at every step. This enables them to react faster to customer needs, further enhancing service efficiency.

With the help of Camunda, the US Department of Veterans Affairs has been able to expedite services for veterans and their families during critical times of need.

Read our case study with the US Department of Veterans Affairs.

Integrated systems enable data-driven decisions and personalized offers

Integrating different software systems allows for seamless data flow and reduces the need for manual data entry. This ensures that all departments have access to accurate, up-to-date information, and a unified view of customer data. This integration enhances decision-making and enables more personalized customer interactions.

Amdocs and DigitalONE have transformed the customer experience by utilizing Camunda’s process automation solutions to automate customer care and ordering workflows across all engagement types and channels.

Read our case study with Amdocs.

Standardized procedures help minimize delays

Establishing and following standard operating procedures reduces variability in task performance, which translates to a more consistent customer experience. Clear approval paths minimize confusion and delays, streamlining the process and improving overall efficiency.

Vodafone has successfully automated and optimized the entire customer lifecycle, including sales, service, and retention with Camunda. This has resulted in significant time savings in customer service and allows staff to focus on customers and value-added activities.

Read our case study with Vodafone.

Real-time insights and predictive analytics reveal bottlenecks, facilitating process improvements

Process orchestration provides end-to-end visibility into a process that is difficult to get otherwise. It then leverages advanced data analytics to predict and address potential bottlenecks, allowing for proactive adjustments that maintain smooth operations. Enhanced reporting provides detailed insights into process performance, facilitating continuous improvement and better decision-making.

With the implementation of Camunda, T-Systems Austria has significantly enhanced data quality, boosted customer satisfaction, and unlocked new business opportunities.

Read our case study with T-Systems.

Improved collaboration and communication help align business and development goals

Using an open and easily understood process modeling system like BPMN ensures that all team members from both the business and IT sides are informed and aligned, reducing the risk of miscommunication. Clearly defined roles and responsibilities help each team member understand their tasks and contributions, fostering a collaborative environment that ultimately enhances the customer experience.

Babylon Health has provided more control to patients thanks to automating workflows, ensuring a better flow of information and more secure and consistent services.

Read our case study with Babylon Health.

Process orchestration provides the flexibility needed to adapt swiftly to market changes and customer demands

Another key benefit of process orchestration is its scalability and flexibility, both of which have a direct and positive impact on the customer experience. With end-to-end orchestration and a composable architecture, businesses can easily adapt to growing demands, maintaining a consistent and high-quality customer experience even as they scale.

This adaptability means that as the volume of customer interactions increases, the system can handle the load without compromising on service quality.

Additionally, the agility provided by process orchestration allows businesses to quickly respond to changing customer needs and market conditions. This responsiveness ensures that customer interactions remain personalized and effective, meeting the evolving expectations of customers and providing a superior overall experience.

Connected systems provide better experiences for customers

As our report, State of Process Orchestration and Automation 2025, highlighted, organizations that have successfully implemented process orchestration experience significant improvements in customer service. This success stems from the ability to coordinate various tasks and streamline workflows, ensuring more consistent and reliable interactions with customers.

By orchestrating processes effectively, businesses can integrate customer data into automated workflows, enabling tailored and timely interactions. This enhances personalization and ensures that all systems are connected and optimized. As a result, they can deliver faster and more accurate services, allowing them to focus on personalizing customer interactions.

From the customer’s perspective, this translates to quicker response times and a smoother, more frictionless experience, leading to higher satisfaction and fostering loyalty. Ultimately, well-orchestrated processes create a more consistent and personalized customer experience, building trust and loyalty over time.

Discover how Camunda’s process orchestration solutions can transform your customer experience. Start a free trial today!

Read more about customer experience and process orchestration

• Delivering Omnichannel Customer Experiences with Process Orchestration
• The Role of Automation in Enhancing Customer Experience
• How Funding Societies is Speeding Lending, Delivering Superior Customer Experiences, and Making Banking more Inclusive with Camunda

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