Governing AI agents in enterprises is achievable. Here’s how

You’ve heard of AI agents, but what do they mean for your business operations in practical terms? How is an AI agent defined, how does it operate, and more importantly, who or what ‘controls’ one? 

As a software entity, an AI agent is designed to perceive its environment, make decisions, and act autonomously to achieve set goals. They leverage artificial intelligence techniques such as machine learning, natural language processing, and reasoning to interact with their surroundings, including humans, other systems, and tools. They range from simple, task-specific bots, to sophisticated agents capable of addressing dynamic, multi-step goal-oriented actions. 

Which means effective control and governance are needed to accompany them within a business context.  

• How much autonomy should an AI agent have? 

• When and where should a human be involved in reviewing or overriding the decisions of an AI agent? 

Environment and context play a key role in them functioning effectively. Providing structure — both in the input and context that guides an agent’s behavior, and in the generated output — is essential to ensure reliable outcomes. Structured outputs are particularly crucial considering integration within workflows and processes, and an agentic automation architecture can give you that level of control. 

Defining the autonomy of your AI agents with guardrails 

Asking "what should an AI agent be allowed to do on its own?" is a fundamental question when adding the technology into enterprise processes. 

The good news is that defining and managing AI agents in a controlled and governed manner is an achievable priority. Flowable’s automation software allows you to define the autonomy for each of your AI agents based on what your organization is comfortable with. 

Some agents might require strict governance, with every step subject to human review. For example, you might add a human in the loop directive within a process to validate decisions or verify data extracted by an agent at certain stages before moving forward. This is one way to ensure control over outcomes in precision and compliance critical processes.  

At the other end of the spectrum, fully autonomous agents can operate in more of an ‘autopilot’ mode, independently taking decisions and actions. In this case, the Flowable Platform gives you the power to monitor and govern AI agent performance, ensuring they remain aligned with your defined goals. 

Deploying your AI agents with Flowable also allows you to put guardrails in place for fully autonomous agents by providing available data, context, and expected outcomes so that they dynamically select the next best action based on these, to ensure you’re driving efficiency and scalability without compromising oversight and direction. 

You can also opt for more restrictive configurations initially to build trust and gain experience with AI agents, while also hashing out the guard rails you’ll need in place. Over time, as confidence in the system grows, you can adjust this and gradually extend the autonomy of your agents within specific processes while maintaining full compliance and end-to-end regulation.  

Onboarding your AI agents as new efficiency enablers within your teams  

Large language models can be used as the foundation training for an AI agent to equip it with access to vast, global knowledge. This enables the handling of a wide range of tasks, from extracting commonly known information, like addresses, to composing meaningful content, such as emails or customer responses. While this is powerful, it’s also often insufficient when more specific and localized expertise is required. 

Your organization has its own unique requirements. And will have specific rules for decision-making or protocols for handling certain tasks. For example, a bank's credit card application process follows set rules and relies on critical data points. These nuances reflect local knowledge that can be used to effectively onboard your AI agents — much like training a new employee to be up to speed with your internal operations. 

Orchestrating and building AI agents with Flowable means you can leverage “retrieval-augmented generation.” This sets parameters for your AI upon receiving a query to first retrieve relevant documents or data from a set of sources specified by you, in a local knowledge base. That information is then combined with its existing knowledge to ensure apt responses and actions. Which allows you to enhance your agents by enriching them with your organization's local rules, data, and contextual information where and when necessary. 

By combining the broad capabilities of global knowledge with tailored, localized expertise, you gain complete control over what information is available to AI agents. And that ensures tasks are performed with accuracy and precision, while aligning with your organizational needs. 

Providing the right context and data for an AI agent to be business ready 

For your AI agents to operate effectively they need the right context. Which means access to structured data they can understand and build upon. For simple task-based agents, well-defined input and output data may be sufficient. But more complex agents will need a deeper awareness of their operating state to understand when human intervention is needed, when external services should be invoked, or how to adapt based on evolving information. 

Flowable’s case model management, designed for agile process automation, is perfect for just that. By embedding AI agents within a case model — essentially a blueprint for how to handle a specific but fluid situation — organizes all the steps, information, and actions an AI agent needs to take to get the job done specifically as it should, even as unpredictable events occur. 

This provides a structured environment that maintains state, context, and governance throughout an agent’s lifecycle. The case that you assign to an agent ensures it has access to all relevant data, events, and service interactions while also enabling human-in-the-loop oversight where necessary.  

By situating AI agents within automation cases in Flowable, organizations gain precise control over how the agent operates, what data it uses, and how it interacts with its environment. This structured approach ensures that agents are not just intelligent but also context-aware: acting with purpose and accuracy while aligning to business goals. 

A human-in-the-loop approach to autonomous AI management 

Flowable’s process and task automation capabilities are invaluable if you're not entirely confident in an AI agent's accuracy. And if you want to guarantee that a human can review and correct its output when necessary. 

It makes integrating human oversight into AI-driven processes easy and manageable. The level of autonomy for each agent becomes entirely configurable, ranging from full autopilot to observed or assistive modes: depending on the specific task, use case, and context. Even when an AI agent operates autonomously, extracting structured data, making decisions, or providing analysis, a human can still be involved to review and refine the output before it moves forward, following your configuration. 

But beyond optimized manual oversight, Flowable also enables continuous monitoring and improvement. You can track how often human intervention is required and use this feedback to either refine the agent’s learning process or adjust its model and prompts for better results. Additionally, secondary AI agents can be assigned to review outputs and determine whether human involvement is needed, factoring in elements like sentiment when the agent is used in customer interactions. 

This structured approach ensures that AI agents remain both powerful and accountable, delivering efficiency, while maintaining the level of control and accuracy you need.