• Written by Azhar Aliyar, Director

The idea that agentic AI workflows are too complex for energy and utilities is a misconception, as these systems can actually help navigate the unique challenges of this sector, such as outage planning, regulatory reporting burden, and geographically dispersed assets, which can make it difficult to implement efficient workflows.

Unique challenges in energy and utilities

The energy and utilities sector faces distinct challenges that can make it harder to implement agentic AI workflows, including the need for careful outage planning to minimize disruptions to service, a heavy regulatory reporting burden that requires accurate and timely submissions, and geographically dispersed assets that can make it difficult to monitor and maintain equipment. These challenges can make it difficult to design workflows that are both efficient and effective.

For example, a shift supervisor at a power plant may need to check the maintenance log to ensure that all necessary repairs have been completed before scheduling an outage, while also ensuring that the plant is in compliance with all relevant regulations. This requires a high degree of coordination and planning, which can be facilitated by agentic AI workflows.

Defining agentic AI workflows

So what separates an agent from a chatbot? The key difference lies in the agent's ability to use tools, maintain state, and work towards a goal. An agent is not just a passive responder to questions, but an active participant in the workflow, using available tools and data to achieve a specific objective. This requires a high degree of autonomy and decision-making capability, which can be challenging to implement in complex environments like energy and utilities.

A good example of an agentic AI workflow in energy and utilities is a system that uses data from sensors and other sources to predict when equipment is likely to fail, and then schedules maintenance accordingly. This requires the agent to use tools like predictive models and scheduling software, maintain state by tracking the status of equipment and maintenance schedules, and work towards a goal of minimizing downtime and reducing maintenance costs.

Human approval and oversight

One of the key questions in implementing agentic AI workflows is where to include human approval and oversight. While it may be tempting to remove human approval steps altogether, this is usually premature, as humans are still necessary to provide context and judgment in complex decision-making processes. Instead, human approval should be targeted to specific points in the workflow where judgment and oversight are most critical.

For instance, an agentic AI workflow may be designed to automatically schedule maintenance, but human approval may be required before the schedule is finalized. This ensures that the schedule is reasonable and takes into account any unusual circumstances that may not be captured by the agent's algorithms. To learn more about how AsscherAi can support your organization's workflow, visit our website.

Failing differently: logging and error handling

An agent fails differently from a model, as its failures are often more nuanced and dependent on the specific context in which it is operating. While a model may simply produce an incorrect prediction, an agent's failure may be more complex, involving a series of incorrect decisions or actions. This requires a different approach to logging and error handling, one that takes into account the agent's autonomy and decision-making capability.

For example, an agentic AI workflow may include logging of all decisions and actions taken by the agent, as well as any errors or exceptions that occur. This can help to identify the root cause of any failures and improve the overall performance of the agent. If you have questions about implementing agentic AI workflows in your organization, contact us to learn more.

Limitations and wrong choices

While agentic AI workflows can be a powerful tool in energy and utilities, they are not a panacea for all workflow challenges. There are certain situations in which they may not be the best choice, such as when the workflow is relatively simple and does not require autonomy or decision-making capability. In these cases, a more traditional approach to automation may be more effective.

Additionally, agentic AI workflows may not be suitable for environments with extremely high levels of uncertainty or unpredictability, as the agent's algorithms may not be able to cope with the complexity of the situation. In these cases, human judgment and oversight may be more critical, and the use of agentic AI workflows may need to be carefully considered.