A holistic approach to agentic AI workflows

AI adoption is expanding. The bigger challenge is to move beyond simple task automation to more dynamic and valuable business use. Beyond isolated AI-driven tasks is also where agentic AI’s full potential lies. For this, multiple AI agents must be able to work together, and interact with business processes, human decision-makers, and enterprise systems in a structured, scalable, and ‘orchestrated’ manner. 

Process orchestration provides the holistic framework for this next stage of AI use. And it ensures your AI agents don’t operate as isolated actors but become a fundamental and connected element in your organizational workflows.  

Overcoming fragmented AI use with agent orchestration 

Many organizations have adopted AI in some form, such as chatbots, document-processing, or machine learning models in areas like risk assessment. 

But these tasks are typically deployed in isolation without being connected in the larger business workflow. Such a ‘point by point’ use creates inefficiencies as each new AI solution needs to be built from scratch. It also limits AI’s ability to single task executions — where ideally, you really want AI to make informed decisions based on the broader context of a process within the business. 

A bank may use an AI-powered chatbot to assist with customer inquiries. And another AI model might assess the risk of a loan application. If these AI agents aren’t connected, a customer applying for a loan might have to re-enter information when moving from one point to the other, or employees be forced to transfer data between the systems manually. Either way, the result is disjointed for both customers and staff, with AI not delivering as much benefit as it could. 

Orchestration solves this problem by providing a single platform to develop and connect your AI apps. Which means your AI solutions interact within other apps, business workflows, and alongside humans. In our banking example, the orchestrated AI system can then automatically pass relevant data between the chatbot, risk assessment engine, and human loan officer, creating a single experience for both user and operator without adding unnecessary delays or manual intervention. 

Connecting agentic intelligence in automation workflows 

The power of AI grows exponentially when it forms part of a larger automation strategy. Rather than treating AI models as a collection of independent tools, process orchestration can integrate multiple AI agents within existing business workflows, from one end to the other.  

An insurance company, for example, may use AI to handle claims queries, analyze claims, detect fraud, and assess risk. Without AI orchestration, these tasks would operate independently, saving time over the manual alternative but still requiring staff to manually move an existing process from one tool to the next and to prompt the actions at each step. 

With orchestration, the system can automatically identify issues across all of the tasks and route to an investigator, escalate urgent cases, or request additional information from the customer accordingly.  

This shift from automation to holistic intelligence with agentic workflows is immensely powerful as you enable AI to do much more than just execute simple tasks. Orchestrating AI agents within structured processes empowers AI to apply more context, make more informed decisions, and work alongside human employees with memory to deliver the best possible results. 

Keeping agentic AI accountable within your workflows 

One of the biggest concerns with AI adoption is governance. We all need to ensure that our AI-driven decisions are accurate and can be traced, controlled, and justified. These guard rails are especially important in regulated industries like finance, healthcare, and legal services, where compliance is carefully observed. 

When AI sits within a process automation framework that works as an agentic architecture, you can apply clearly defined boundaries. This control ensures that any automated decisions made by AI agents align with your business rules and regulatory requirements. Automated testing can also be applied to AI models during development, with advanced scenario testing designed to prevent any unexpected behavior downstream. Detailed audit trails offered by an orchestration platform also provide you with visibility into any AI-driven decisions, allowing you to track precisely why a particular action or decision occurred at any point. 

Consider a financial institution using AI for fraud detection, for example. If an AI model flags a transaction as suspicious, it’s essential to understand why. The orchestration platform ensures that every decision made by the AI is documented, providing transparency into the factors that influenced the decision and ensuring no bias is involved. If needed, a human reviewer can step in to override or validate the AI’s assessment. This failsafe mode ensures that AI remains accountable at all times and allows adjustments to be made for future actions.

Try out Flowable AI agent building, governing, and orchestrating for free in Flowable AI Studio with the Flowable Platform trial.

Scaling your AI agents across the enterprise 

As businesses look to expand their use of AI, the ability to scale effectively and securely becomes critical. And an orchestration platform provides a controlled way to deploy AI at scale, ensuring that AI agents can be developed quickly and work efficiently when deployed, while still maintaining close alignment with overall business priorities and governance requirements. 

Imagine a multinational company using AI for compliance monitoring. This organization needs to adhere to different regulatory requirements across geographical regions, which means their AI models also need to conform to those regulations. By using a process automation platform, the company can apply multiple rules to the various markets while maintaining centralized oversight. This level of governance still offers the benefits of scalable AI without introducing unnecessary operational risks. 

Beyond compliance, an orchestration platform also makes it easier to manage AI workloads. Instead of deploying AI tasks in isolated pockets, businesses can use orchestration to optimize IT resource allocation, ensuring that AI servers and processes are load balanced and cost-efficient. Whether handling customer inquiries, processing invoices, or analyzing data, an orchestrated AI system can distribute tasks intelligently, choose the best agent or most cost effective for certain tasks, avoiding bottlenecks, maximizing efficiency, and optimizing spend. 

The future of AI in business automation 

For many, AI is already transforming business operations, but its impact will grow even more when organizations move beyond standalone AI applications with orchestration. The future of AI-driven automation is not about making individual tasks faster. The future of AI is about helping your business build agents faster, making them smarter and more adaptable, and ensuring every AI-decision is transparent, audited, and compliant. 

Orchestration platforms allow AI agents to work hand-in-hand with human operators in end-to-end business processes. By ensuring that AI is a core component of business process automation we ensure structured, governed, and objective-aligned activity. AI agent use that adapts to business and process changes, and scales efficiently across the enterprise is with a holistic approach, for those ready to embrace it.