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.

To get the most out of BOAT, you need a composable approach that allows you to combine best-of-breed task automation tools and business applications.

Composability is particularly important when it comes to the use of AI in business processes. A composable BOAT solution enables:

• Modular AI adoption: You can plug in different AI agents, models, tools, and services as needed rather than being locked into a single AI vendor
• Scalable automation: You can start with small AI-powered automations and expand them as AI capabilities mature and your business needs evolve.
• Seamless integration: AI components can interact with the tech stack you already have, including legacy systems and homegrown software, cloud applications, and third-party services.
• Rapid adaptation: As AI regulations, business needs, and technology landscapes change, you can adapt your business processes without overhauling your entire automation infrastructure.

The challenges of operationalizing AI

Unlocking the potential of AI to transform your business operations, improve efficiency, and drive innovation requires more than simply implementing large language models or deploying AI-powered tools. You must operationalize AI by integrating AI capabilities into your end-to-end business processes—whether they’re internal, customer-facing, or both.

Operationalizing AI means using AI where it makes sense. You want to balance automation with human oversight and ensure that AI-driven decisions align with business goals and regulatory requirements. For example:

• A financial services company can use AI agents to detect potential fraud but ensure that cases with uncertainty are escalated to human investigators.
• A customer service department can use AI-powered chatbots to handle routine inquiries while routing complex issues to knowledge workers.
• A marketing team can leverage generative AI to draft promotional emails but require a marketing manager to review them before they’re sent out.

However, operationalizing AI is challenging because:

• Effective use of AI requires an adaptable approach. Many automation platforms have a proprietary, monolithic architecture that doesn’t provide the flexibility or scalability that you need to get the most out of using AI in your business processes.
• Siloed AI leads to fragmented automation and inconsistent outcomes. The excitement around AI tools and services often means that teams have started using them in silos instead of integrating them into processes. This makes it hard to coordinate AI-driven tasks with tasks executed by other IT systems, and makes it hard to ensure that AI has human oversight when needed.
• AI automation often lacks transparency. Many organizations struggle with governance and accountability when AI-driven decisions impact business processes. A lack of transparency makes it hard to measure AI performance, ensure compliance, and mitigate risks.

How Camunda helps you operationalize AI

Camunda is a composable BOAT solution that enables you to operationalize AI in your business. We provide an open, flexible, and scalable approach to process orchestration, so you can:

• Stay flexible. Combine Camunda’s built-in process orchestration and automation capabilities with other tools, services, and systems, including your own AI models and AI services such as OpenAI, Google Gemini, and so on. This flexibility ensures you can add AI to processes in the exact way that makes sense for your business goals without compromising because of technology limitations.
• Integrate AI tools and services seamlessly into business processes. Ensure they work cohesively with other automation technologies as well as with people. Seamless integration means that you can embed AI in processes while maintaining accuracy, efficiency, and reusability, all while preventing siloed AI. As a bonus, it ensures that AI models and tools have access to the full process context, enabling them to make more informed decisions.
• Ensure AI governance through process design, real-time process monitoring, and integrated human oversight. Camunda uses the BPMN process modeling standard and a “one model approach.” That means the visual process model that you design is the same visualization that you see when monitoring processes and when analyzing their performance. BPMN makes it easy to ensure that human reviews and approvals are built into processes where you need them. BPMN also makes it easy to increase or decrease the level of human oversight in a particular process as your needs change.

Learn more

If you’re ready to get started now, check out how to build your first AI agent in Camunda.

The post How a Composable BOAT Solution Helps You Operationalize AI appeared first on Camunda.

“Agentic automation can augment cognitive tasks, process unstructured information, and take independent actions. These capabilities unlock new possibilities for autonomous business operations, AI-assisted development, and human augmentation. AI-powered agents will need to be coordinated, which is driving the need for platforms that better support orchestration and automation.”
– Gartner, Predicts 2025: The Future of Automation Is Autonomous, 12 December 2024

Camunda gives you the flexibility that agents require

AI agents are powerful, but without orchestration, they lack the coordination, accountability, and reliability needed for real-world, business-critical processes. Camunda’s agentic process orchestration coordinates AI agents, ensures their work is transparent, and provides the reliability your customers are looking for.

It:

• Provides full visibility into process design through a combination of AI and BPMN
• Supports seamless integrations across all process endpoints, from AI to RPA bots and more
• Facilitates collaboration between people and AI by combining AI-driven decisions with human oversight when needed
• Includes powerful monitoring of AI-driven processes and tools to continuously improve process performance

Blending different types of orchestration

Camunda’s approach to agentic process orchestration blends deterministic and nondeterministic process execution to give you the best of both worlds. You can use BPMN to model out the parts of processes that require high levels of predictability and control, and let AI agents handle work that involves creativity and proactive decision-making.

This approach works well for case management scenarios such as insurance claim handling, fraud investigation, and customer complaint resolution. Agents provide a way for you to increase the level of automation in processes where the exact steps are hard to predict, freeing up knowledge workers to focus on other projects.

Learn more

Download a free copy of Gartner Predicts 2025: The Future of Automation Is Autonomous to learn about:

• Unlocking new value by applying Al to an integrated suite of tools such as business orchestration and automation technology (BOAT) platforms
• Using AI to enhance the usability of automation platforms for software developers, low-code developers, and business technologists
• Improving the ability of intelligent document processing tools to extract insights from documents in business processes

The post Prepare for Autonomous Automation with Agentic AI appeared first on Camunda.

• Serve and interact with their customers and users
• Design and deliver products and services
• Collaborate with their partners and suppliers
• Enable and empower their employees

What isn’t changing is the importance of process. Business processes define how you deliver products and services to your customers; how you work with your suppliers and partners; and how your employees get things done. AI will change the way we build, run, and improve business processes, but the processes themselves won’t disappear.

That’s why process orchestration is a critical component of AI adoption and strategy. The steps that make up a business process are executed by people, systems (including AI), and devices—we refer to these as endpoints. Process orchestration ensures that even the most complex series of steps are executed correctly across all endpoints. In turn, that ensures customers, suppliers, partners, and employees have a smooth experience when they interact with or are affected by processes.

Implementing AI-powered automation without making it part of a process orchestration strategy means you run the risk of creating disconnected automations that lead to operational inefficiencies and compounding technical debt. According to Accenture:

Generative AI and AI are now the highest contributors to a company’s tech debt along with enterprise applications, according to our digital core research. This trend will likely exacerbate: in our Pulse of Change survey, 52% of organizations said they plan to allocate more funds toward generative AI, heading into 2025.

– Accenture, Build your tech and balance your debt, 22 October 2024

Also, organizations in highly regulated industries are naturally concerned that the use of AI will disrupt carefully designed policies and procedures that ensure business processes comply with government and industry regulations.

The research that Camunda commissioned for the 2025 State of Process Orchestration and Automation Report shows that 84% of IT and business leaders are looking to add more AI capabilities over the next three years. In addition, 93% of respondents agree that AI applications and services will need to be orchestrated across business processes in order to get the maximum benefit from investment in AI.

These leaders aren’t taking a “wait and see” approach to AI adoption, but they’re aware of the dangers of a siloed AI strategy.

Let’s take a look at how process orchestration can help you leverage AI today, prepare for the AI innovations of tomorrow, and manage the potential risks of AI adoption.

Deterministic process orchestration

Deterministic process orchestration has been the dominant approach to process automation for many years. Deterministic means that what will happen each time an instance of the process is executed is determined ahead of time, usually by a business process model defined in a language such as Business Process Model and Notation (BPMN). Given the same inputs, configuration, environment, and conditions, the process will always follow the same sequence of steps and yield the same results.

There are many benefits to deterministic process orchestration:

• The process is predictable and reproducible, and can easily be audited.
• The use of a process model makes it easy for teams to visualize, explain, and collaborate on every aspect of a process’s design.
• If the process model is defined in BPMN, it’s self-documenting: what you see is exactly what will be executed.
• The cost of executing the process (for example, measured in hardware resource consumption) is relatively low, and execution is typically quite fast,

However, there are also downsides to deterministic process orchestration:

• To build a process model, you have to truly understand and agree on what should happen in a business process. Sounds simple, but many organizations struggle with inconsistent or poorly documented processes.
• Building the process model itself takes time and requires testing.
• It’s difficult to deliver highly personalized customer experiences if doing so means maintaining many different variations of a particular model.

Dynamic process orchestration

The recent explosion of widely available AI tools and services has many process specialists wondering, Is there another way? Can AI get us to a world of dynamic process orchestration?

Dynamic, also sometimes referred to in this context as non-deterministic, means that there isn’t a process model or any other artifact that defines exactly what will happen each time a process instance is executed. Instead, what happens is determined as the process instance is executed, leveraging AI models and runtime data.

Dynamic process orchestration isn’t common—yet—but we can already imagine some of the benefits it could offer:

• Faster time to value because the process design and testing phase would be dramatically reduced
• Better enablement of business stakeholders who want to automate processes but lack the skillset or IT resources to do so
• Easier delivery of personalized customer experiences based on data about a particular customer’s preferences and needs

In a deterministic world, every part of a process must be planned and modeled. If there are aspects of a process that can’t be determined beforehand, organizations rely on knowledge workers to step in and handle the work.

For example, in case management scenarios such as insurance claim fraud investigation and customer complaint handling, it’s hard to foresee exactly what will need to be done to resolve the case. You typically need a qualified person to look at the case, figure out what needs to be done, and sometimes, actually do the work. Dynamic process orchestration offers a way to increase the level of automation in these hard-to-define processes, freeing up knowledge workers’ time for other tasks.

But dynamic process orchestration has its downsides, too:

• There isn’t a process model that you can point to as the source of truth for what will happen in a business process.
• Auditing processes is more difficult, especially if you don’t have a record of the reasons an AI took certain actions.
• It’s hard to predict what results an AI will produce, even when given the same inputs, configuration, environment, and conditions.
• The cost of executing the process is relatively high and can be vulnerable to latency issues because the AI model is constantly being invoked.

Let’s talk about AI agents

You may have heard terms such as AI agents or agentic AI. Understanding agents is key to understanding the future of dynamic process orchestration.

According to Google’s recent Agents whitepaper:

… a generative AI agent can be defined as an application that attempts to achieve a goal by observing the world and acting upon it using the tools that it has at its disposal. Agents are autonomous and can act independently of human intervention, especially when provided with proper goals or objectives they are meant to achieve. Agents can also be proactive in their approach to reaching their goals. Even in the absence of explicit instruction sets from a human, an agent can reason about what it should do next to achieve its ultimate goal.

– Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic, Agents, September 2024

The autonomous nature of agents makes them ideal for executing a business process in which the process logic isn’t predefined. However, as my colleague Niall wrote in his blog post about AI agents and orchestration, the issue with agents is that they don’t produce predictable, repeatable results, which is what most organizations need them to do if they’re going to rely on agents to replace deterministically defined processes.

The best of both worlds: blending deterministic and dynamic orchestration

How can you gain the benefits of dynamic process orchestration without sacrificing the benefits of deterministic orchestration and—more important—without putting customer experiences and regulatory compliance at risk? The answer is blending deterministic and dynamic approaches in the same process, which is exactly what process orchestration enables you to do. When the dynamic parts of a process are executed by one or more AI agents, we call this agentic orchestration.

Imagine a common business process, such as a Know Your Customer (KYC) process. It involves a very well-known and highly regulated series of steps like:

• Collecting information such as the potential customer’s full name, date of birth, and home address
• Verifying that information using government-issued IDs and databases
• Checking whether the person appears on global or local sanction lists or is a known financial criminal
• Assessing the risk level associated with the person and, in some cases, performing additional due diligence
• Conducting ongoing monitoring of the person’s financial transactions and reporting suspicious activity to the relevant authorities

AI technology, such as optical character recognition (OCR), is already common in KYC processes—for example, it’s often used to extract data from photos or PDFs of ID cards and passports. This saves time and money, but OCR tools aren’t capable of making decisions or being proactive in the absence of human intervention like AI agents are.

An AI agent can enhance a KYC process by taking proactive actions such as:

• Guiding a person through the KYC process, responding to their questions, and dynamically determining how to solve issues such as an unreadable ID or a mismatch in personal data
• Monitoring for KYC policy changes, updating the KYC rules that human knowledge workers use, and changing the way the agent executes the process accordingly
• Automatically adjusting a person’s risk level based on ongoing monitoring and taking action such as restricting their bank account activity or dynamically adjusting their spending limits

A realistic outlook

Have AI agents reached a level of maturity where agentic orchestration is realistic for the processes that are most fundamental and important for your business? And is your organization ready to embrace agentic orchestration?

Forrester provides a realistic outlook in their Predictions 2025: Automation report, which says that in the coming year:

GenAI will orchestrate less than 1% of core business processes. GenAI will affect process design, development, and data integration, reducing design and development time as well as the need for desktop and mobile interfaces. Business users will develop initial workflows, create forms, and visualize the process. But this genAI efficiency still leaves current digital and robotic process automation platforms orchestrating the core process, subject to their deterministic and rule-driven models. To prepare for 2025, recognize that deterministic automation will remain in control of the core long-running process, while AI models will support bursts of insight and efficiency.

— Forrester, Predictions 2025: Automation, 22 October 2024

Now is the right time to ensure your process orchestration strategy is well-defined and your most critical processes are being orchestrated by a platform that allows you to blend deterministic and dynamic execution.

Keep in mind that you don’t have to wait for new use cases or business processes to surface before you think about how to enhance them with agentic AI. Take a look at your existing processes and consider how you might blend deterministic and non-determinist orchestration to achieve a better outcome. Doing so will enable you to experiment with agentic AI today and be prepared to leverage AI increasingly in the future.

Learn more

To learn more about the future of process orchestration, check out our blog post, Prepare for Autonomous Automation with Agentic AI, which breaks down “Gartner Predicts 2025: The Future of Automation is Autonomous,” and where you can get your own free copy.

The post Operationalize AI by Blending Deterministic and Dynamic Process Orchestration appeared first on Camunda.

Despite market convergence, there are still differentiators between process orchestration and automation tools that make them suitable for different use cases. These are five areas where Camunda is unique in the process orchestration and automation world:

• Composability

• Openness

• Collaboration

• Scalability

• Intelligence

Composability

A composable process automation architecture allows you to select and integrate best-of-breed tools, technologies, and services, so you can ensure that your automation architecture precisely fits your business use case—instead of compromising because of technology limitations.

Camunda’s composable approach:

• Leverages cloud-native technologies such as event streaming and Kubernetes to deliver speed, scale, and resilience
• Allows you to orchestrate any tool or service with a Connector marketplace and a robust Connector SDK that make integration easy as well as fully customizable
• Enables best-of-breed choices without compromise; for example, you can combine Camunda’s native task automation capabilities (such as RPA and IDP) with other tools and custom code for maximum flexibility

According to Nicki Todd, SVP of Technology at First American:

“Camunda’s agile, microservices-based architecture has provided us with the flexibility to rapidly adapt and configure workflows, empowering us to meet customer demands with minimal reliance on engineering.”

Openness

An open architecture is a must for composability. Implementing a process orchestration and automation platform with an open architecture makes it far easier to integrate different tools and systems. This provides IT teams with the flexibility to meet specific business requirements and positions IT as an enabler rather than a blocker.

Camunda has prioritized openness from day one, which means:

• Camunda software fits into your tech stack by working with your teams’ selected source control tooling, CI/CD pipelines, and programming languages
• Leveraging the BPMN and DMN standards for process and decision modeling, facilitating collaboration and alignment between IT and the business and abstracting process logic away from task automation to ensure integration flexibility
• Providing well-documented APIs and SDKs and polyglot Zeebe clients that make integration and customization easier for software development teams

Marko Lehn, Team Lead Software Engineering at Zalando, explains:

“To further automate our business processes, we are using Camunda. The open platform permits the implementation of our individual needs in a way that closed business process management suites just cannot achieve.”

Collaboration

It’s common for IT teams and the business to be misaligned on their understanding of what is required to make progress toward digital transformation goals. According to the 2025 State of Process Orchestration & Automation Report, 62% of IT decision-makers and business leaders say that at their organization, IT and business users cannot easily collaborate on individual processes or projects, and 82% believe that miscommunication between teams leads to the wrong thing being built and rolled out to customers.

In addition to the use of BPMN and DMN, Camunda supports:

• Next-level collaboration because IT and business stakeholders build and test processes in a shared modeling environment
• Reusable processes, decision tables, forms, and connectors that make onboarding new projects and new teams easy
• Integrated intelligence that gives you complete visibility on process health and performance so you can make data-driven improvements

Alexander Lorenzen, Domain Architect at BEC Financial Technologies, says:

“I think of a mortgage process as a constellation of several subprocesses, each of which should be orchestrated by its own right… Using BPMN to model the business process as-is allowed us to bridge the gap between the business and the ‘techies.’”

Scalability

Camunda’s next-generation workflow engine, Zeebe, is designed to accommodate high-throughput use cases out of the box. While traditional workflow engines rely on a central database that causes a performance bottleneck, Zeebe uses event-streaming technology instead, enabling it to deliver unparalleled scalability and performance. It maintains the state of running process instances in a way that scales up with high-transaction volumes, that’s resilient to failures, and that performs well at scale.

With Camunda and Zeebe, you’re guaranteed:

• ​​Unlimited horizontal scalability because Zeebe’s use of event streaming avoids database bottlenecks and can scale process throughput infinitely
• Enterprise-grade resilience thanks to a distributed architecture that ensures continuity in the case of hardware or network failure
• A failover architecture that supports replication across global data centers for high availability worldwide

As Larisa Kvetnoy, Director of Operations Technology and Strategy at Barclays, put it:

“We want to reach the point where Camunda handles all our volumes. We expect this to be in the tens of millions of process instances, every single day. Camunda 8 is cloud-native, so we can add instances easily and always maintain enough headroom to handle an unexpected peak.”

Intelligence

AI is already changing the way companies do business. According to PwC’s 2024 Cloud and AI Business Survey, 41% of respondents said they’ve already seen improved customer experiences through the use of generative AI and 40% reported that they’ve achieved increased productivity.

When it comes to the role of AI/ML tools in core business processes, almost all of the IT and business leaders we spoke to for the 2025 State of Process Orchestration & Automation Report believe that AI will need to be orchestrated within automated business processes just like any other endpoint.

Camunda can help you prepare for the increasing importance of AI in business processes:

• Apply AI/ML where it makes sense without sacrificing reliability or regulatory compliance
• Avoid creating AI technical debt by integrating AI/ML tools and services into existing end-to-end business processes
• Camunda’s assistive AI features such as copilots put process designers on the fast track to value delivery

Jakob Freund, CEO and Cofounder of Camunda, is optimistic about the potential for AI in process automation, but he warns:

“One piece of advice: Make sure to sort out your operations architecture today (both business and technology!), because ‘just throwing in AI’ will make things worse, not better. Put in a process orchestration layer that gives you the flexibility to plug in AI where it makes sense today and might make sense tomorrow. There are plenty of real-world examples of organizations that did that successfully and are now on a path to systematically adopt AI on the enterprise level.”

Be ready for the future

Our advanced capabilities in these key areas make Camunda a powerful platform for process orchestration, not only today but also in the future. As the process orchestration and automation market evolves, we remain focused not only on maintaining our current advantages but on preparing ourselves and our customers for the future.

The post What Sets Camunda Apart appeared first on Camunda.

Choosing your preferred cloud provider for your Camunda SaaS account helps ensure:

• A solid fit with your enterprise cloud strategy, including security policies and compliance requirements
• Easier integration with workloads that are already running on a particular cloud provider
• Increased flexibility to choose where business process data is stored so you can satisfy data residency requirements
• Opportunities to optimize performance through access to more geographic regions

Take advantage of expanded region support

We know that the ability to specify the region where Camunda clusters run is important to many of our customers. All Camunda SaaS customers can choose from a wide variety of regions, including AWS’s Frankfurt (europe-west2) and Virginia (us-east-1) regions.

Purchase Camunda on AWS Marketplace

Camunda is available for purchase on AWS Marketplace so you can get started fast, whether you’re interested in a free trial or you’re ready to migrate processes to the cloud. If you’re a current Camunda customer and you’re interested in SaaS, please contact your customer success manager.

The post All Camunda 8 SaaS Editions Now Available on AWS appeared first on Camunda.

Choosing the cloud provider for your Camunda SaaS installation enables you to:

• Gain flexibility for your process orchestration and automation implementation at no extra cost.
• Ensure you comply with your organization’s public cloud policies and more easily procure Camunda by choosing an approved cloud provider.
• Reduce latency and optimize process performance through access to more geographic regions.

Sign up for a free trial today

Whether you’re a new Camunda user or an experienced customer, you can sign up for a free trial of Camunda 8 and select AWS. If you have questions about your Camunda trial, visit our community forum.

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What is a monolithic automation platform?

There are a number of monolithic automation platforms on the market today that you can use to automate business processes that involve automated tasks and/or manual tasks that are executed by knowledge workers. As the term “monolith” implies, these platforms are architected as a single, black-box application as opposed to a collection of loosely coupled components or microservices that can operate independently. They typically have an integrated, proprietary design environment where users can build processes, decisions or business rules, and basic user interfaces; as well as proprietary tooling or specific programming language requirements for implementing complex use cases.

Examples of monolithic automation platforms

Traditional monolithic automation platforms such as Pegasystems, Appian, IBM Business Process Manager, and Oracle BPM originated in the world of business process management suites (also known as BPMS or iBPMS). They promised to provide an all-in-one solution for designing, automating, and managing business processes, especially back-office processes for finance teams, HR departments, and IT help desks. As organizations across industries have transformed more and more of their business to be digital, they’ve tried to capitalize on their investment in these monoliths by applying them to the core processes that build and sell their products and services.

The rise of digital transformation has also led to an emerging set of modern monolithic monolithic automation platforms as vendors add lightweight process automation capabilities to products that were originally designed for purposes such as customer relationship management (CRM), IT service management (ITSM), and personal and team productivity. In some cases, these capabilities were built in-house; in other cases, process automation has been loosely integrated with the original product after various acquisitions. Examples of modern monoliths include offerings such as ServiceNow’s AppEngine, Salesforce Flow, and the Microsoft Power Apps suite of tools. These products also promise to be all-in-one solutions for business automation; however, they suffer from many of the same flaws that traditional automation monoliths do.

Why you’re fighting your monolithic automation platform

Automation tooling should help both business and IT teams deliver automated business processes faster so you can reduce operational costs, make your customers happy, and keep up with your competition. But if you find that your teams are fighting against an automation platform’s design instead of being enabled and empowered by it, chances are it’s due to one or more of the following challenges.

High development effort for complex processes

Complex business processes almost always require some amount of software development work to reach a high level of automation. Software developers are most productive when they can write code in the programming languages they know, using the development tooling and software delivery pipelines that they’ve already set up.

Automation monoliths don’t adhere to standard software development practices. Instead, they require users to use proprietary tools to build and implement processes, in the hopes of simplifying the design environment for low-code developers and business technologists. However, proprietary tooling has the opposite effect for software developers; it wastes their time as they have to learn to use that tooling instead of their normal development toolset and standards. In addition, developers have to continuously maintain this knowledge, which can lead to a lack of sufficiently skilled team members in the organization and the need to hire expensive consultants.

When working with automation monoliths, technical teams can’t see inside the black box to understand exactly how it executes processes, which means software developers are often stuck reverse-engineering automation code. Plus, connectors for automation monoliths tend to be built in a proprietary way, making it hard to customize or extend the way that processes interact with various enterprise tools.

Poor transparency and no portability for processes and decisions 

Automation monoliths enable collaboration by providing drag-and-drop tools for building processes. However, visualization is only one piece of the puzzle; for a workflow engine to actually execute a process, it must contain code that can run. Some of this code is generated by the automation monolith itself, but it doesn’t take much complexity for a business process to require custom code as well.

When processes are built using proprietary tools, there’s little to no transparency for the software developers who need to make the process executable. It also means that processes can’t easily be exported from one tool and imported into another. In contrast, open modeling standards such as BPMN and DMN are fully transparent and portable, without sacrificing the type of user-friendly process visualization that enables cross-functional collaboration.

You’re paying too much to be struggling with an automation monolith

Automation monoliths lock you into a proprietary process automation design and execution environment. Organizations that adopt monolithic automation platforms require extensive training and consulting hours from specialists who know how to use the platform. This doesn’t just cost time and money up-front; it also creates maintenance headaches when business processes need to be updated or expanded. 

If an automation monolith is delivering the process automation capabilities you need, that’s great! But if your teams are fighting against it, or if the return on your investment isn’t high enough, then you need to take action.

A composable automation solution is a better way

Camunda is an alternative to monolithic automation platforms that delivers transformational process orchestration. It offers the best of both worlds: loosely coupled components that fully integrate with one another, yet are designed to integrate seamlessly into an existing technical architecture to create a composable solution. 

For additional flexibility, Camunda components can be deployed to on-premises infrastructure, to public or private clouds, or in a hybrid configuration. We also offer a hosted SaaS option that provides fast, massive scalability for high-volume, high-performance use cases.

Sign up for a free customized demonstration or a free 30-day trial to see how a new approach to process orchestration can help you achieve your business automation goals. 

The post Why Your Monolithic Automation Platform Is Slowing You Down appeared first on Camunda.

In June, they published Leverage The Power Of Process Orchestration To Drive Innovation, which describes how process orchestration enables organizations to innovate and increase efficiency. And in May, Gartner introduced the concept of business orchestration and automation technologies (BOAT), which they’ve since covered in more detail in Quick Answer: Beyond RPA, BPA and Low Code — The Future Is BOAT (click that link to download a free copy).

It’s exciting to see industry analysts recognize the importance of orchestration because at Camunda, we’ve focused on delivering enterprise-grade process orchestration to our customers for years. We’ve also learned a valuable lesson along with our customers: You’re going to hurt your business if you compromise when choosing an orchestration engine.

In a way, it’s simple. Processes are the algorithms that define how your business runs. An orchestration engine executes those processes—no matter how automated or manual they are, and no matter how many different tools and technologies they involve. Therefore, if the orchestration engine can’t deliver the capabilities and performance that your business needs, it’s the business that’s going to suffer.

But not all orchestration engines are created equally, and there are many technical factors that determine whether a particular engine is the right choice for your organization. Let’s look at these factors in three categories:

• What matters at design time
• What matters at runtime
• What matters in operations

What matters at design time

Design time features allow users to build, test, and deploy process and decision logic. You should consider these design time capabilities:

• A standards-based approach to process and decision modeling that provides a visual way for technical users and business stakeholders to collaborate on process design. Standards facilitate communication and alignment between the subject matter experts in the business and the software developers who implement automation projects.
• Support for advanced workflow patterns that involve branching logic, dynamic parallel branches, message correlation, task cancellation, time-based escalation, fallback flows, and more.
• Integrated yet loosely coupled decision automation so that automated business decisions and rules can be delivered as part of processes or executed independently from processes.
• Support for process and decision versioning so users can visualize and understand how process and decision models have changed over time, and can easily roll back to previous versions if needed.
• A process simulator that shows how a process will function under different conditions to ensure that process logic works the way it should without requiring IT to provision a special environment.
• The ability to integrate with automated testing pipelines so teams can efficiently perform regression testing on process and decision models. This assurance enables teams to change and improve processes much faster.

What matters at runtime

At runtime, you need an orchestration engine that delivers massive scale, resilience, security, and durability for your most critical business processes. You should consider these runtime capabilities:

• The ability to manage long-running business processes—that is, processes that run for hours, days, or weeks—with a high level of data consistency.
• Scalable, resilient, performant state persistence that maintains the state of long-running process instances while avoiding the performance bottleneck of a central state management database.
• Support for correlating external events to process instances that are running in the orchestration engine, even when a process instance runs for a long time or when there are thousands of process instances running simultaneously.
• Timers and timeouts that enable a process to wait for a period of time and then take action, ensuring that SLAs are met and long-running processes don’t lead to a poor customer experience.
• The ability to carry data payloads through process instances. Business data is often stored in many different places, and while an orchestration engine is normally not the single source of truth for business data, it needs to make the right data available to process instances at the right time.
• Integration that works both synchronously and asynchronously, so the engine can trigger task automation tools to do work; wait for a response; trigger other tasks in parallel; and fall back to a different flow if the task automation tool fails.

What matters in operations

When operating an orchestration engine, you need features that make it easy to install, run, and upgrade the tooling. Look for:

• An open architecture that’s not tied to a specific tech stack or monolithic application architecture, but that instead can be integrated into your existing automation landscape.
• The ability to allow users to modify running process instances, for example by changing the values of process variables, so people can solve problems and keep processes flowing.
• The ability to allow users to migrate running process instances to a new model with minimal or no interruption, depending on how extensively the process model has changed. This ensures you can update long-running processes without interrupting business.
• Support for consistent high availability, ideally through a distributed architecture that doesn’t rely on a single workflow node or central database.
• Support for high uptime and easy in-flight upgrades, so you can take care of routine maintenance and software upgrades without affecting running process instances.
• Reliable software backups that ensure the data in the backup is consistent;  that minimizes the impact on engine performance; and that can run without interrupting process instances.
• A comprehensive audit log that keeps track of every automated and manual action that’s taken, with a configurable level of detail that enables IT teams to pinpoint issues and that’s available in a human-readable format for business compliance purposes.

Learn more

To learn more about what matters when choosing an orchestration engine, check out our deep dive into advanced workflow engines. And to get a checklist for evaluating your software options, download our Buyer’s Guide for End-to-End Process Orchestration.

The post Don’t Compromise on Your Orchestration Engine appeared first on Camunda.

For example, check out our new credit card fraud dispute handling blueprint. This blueprint shows how you can transform a time-consuming and often painful process into a better customer experience by leveraging process and decision automation. Combine this blueprint with our AI service Connectors to further enhance dispute handling with AI/ML capabilities.

Get started faster with process application blueprints

Process application blueprints are a great way to get started with Camunda and BPMN. When you download a process application blueprint to Camunda Modeler (SaaS or self-managed), you can immediately start learning from it and experimenting with changing it. You can:

• Immediately see how BPMN elegantly represents the complexity of a business process so that everyone who has a stake in the process will be able to visualize exactly how the process would flow.
• Use Play Mode in Camunda Web Modeler to mock how the process would run in a real-life scenario, complete with sample process data and the ability to simulate error conditions.
• Learn about both basic and advanced workflow patterns in BPMN and experiment by changing the process model and seeing how those changes affect the process flow in Play Mode.

Check out Camunda’s new industry-specific blueprints

In addition to the credit card fraud dispute handling blueprint, we’ve added blueprints for:

• Financial services: The loan origination and processing blueprint demonstrates how to manage a loan application that a consumer starts by interacting with a chatbot.
• Trade services: Manual trade exception remediation processing is difficult and prone to errors when done manually, so this blueprint shows how you can execute pretrade reconciliation to identify errors and exceptions before trading is filled.
• Insurance: Personal insurance claims are typically high volume and very expensive to process without automation; this blueprint shows how to automate repetitive tasks and involve insurance adjusters only when needed.
• Telecommunications: Retail mobile orders can be complex and vulnerable to fraud; the mixed service order management shows how to orchestrate mobile orders that involve both hardware delivery and mobile number provisioning.
• Retail: The car rental booking blueprint illustrates an end-to-end process that accounts for situations where a reserved car isn’t available or a rented car isn’t returned on time.

Contribute to Camunda Marketplace

Blueprints are a great way to share your industry and BPMN expertise! We welcome blueprints from all types of businesses, from financial services and insurance to telecommunications and pharmaceuticals. Whether you’ve designed a process model, a form, or both—just follow the guidelines here. If you’d like to chat with other Camunda users about blueprints or Connectors, visit our online forum.

Try blueprints today

Whether you’re new to Camunda or you’re a Camunda 7 user who wants to see what Marketplace is all about, the best way to find out is to sign up for a free 30-day trial of Camunda 8. You can browse Marketplace directly in Camunda 8’s Web Modeler and start using blueprints right away.

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