Pegasystems recently introduced Pega Agent Experience, a new set of API capabilities aimed at enhancing AI agents within their workflow automation solution. I had a chance to speak with CTO Don Schuerman, who highlighted how this technology transforms workflows into reliable agents capable of seamless interaction with both processes and humans. Schuerman emphasized the importance of using workflows instead of relying solely on prompt engineering, which can be unpredictable. By allowing agents to follow structured workflows, businesses gain consistency and predictability in operations. This integration of large language models with workflow orchestration represents a significant advancement in creating effective AI-driven solutions that work well in regulated environments.
Earlier this week, Pegasystems announced the launch of Pega Agent Experience. This new set of API capabilities enhances their workflow automation and orchestration solution, aiming to create more reliable AI agents. I recently spoke with their CTO, Don Schuerman, who shared fascinating insights about how these innovations transform workflows into agents and their integration with Pega Blueprint. This platform leverages generative AI to simplify enterprise application and workflow design.
In our discussion, Don explained how Pega Agent Experience works seamlessly with Pega Blueprint. Together, they allow businesses to convert workflows created in Blueprint into agents that can communicate efficiently with both processes and humans. This combination delivers greater reliability and predictability compared to traditional prompt engineering tactics.
Don highlighted an important aspect: the focus on workflows rather than on the complexities of prompt engineering. He stated, “In many cases, the best way to utilize an agent is to have it follow a predetermined workflow that reflects the best business practices.” Relying solely on prompt engineering can lead to unpredictable outcomes, making it essential for agents to adhere to clear guidelines.
He further elaborated on the significance of using structured workflows to ensure that agents can navigate and explain their operations across various channels and interactions. “By defining the steps an agent needs to follow, we can achieve predictability,” Don noted. This is crucial, particularly in regulated environments where businesses must maintain compliance.
As interest in AI agents grows, many still grapple with determining the balance between automated decision-making and essential human oversight. The challenge lies in ensuring that agent decisions are predictable and reliable within the constraints of a regulated enterprise.
Overall, the fusion of large language models with workflow orchestration represents an exciting frontier in AI development. By harnessing the potential of agentic AI alongside robust workflows, organizations can optimize their processes and achieve remarkable efficiency.
For further insights, check out the interview clip where Don Schuerman dives deeper into these advancements.
Tags: Pegasystems, Pega Agent Experience, workflow automation, AI agents, Don Schuerman, Pega Blueprint, enterprise applications, generative AI, business efficiency.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can act on their own, making decisions and taking actions without constant human supervision. This allows them to adapt to different situations and improve efficiency in various tasks.
How does workflow orchestration improve business reliability?
Workflow orchestration connects different tasks and teams smoothly, ensuring that everything runs according to plan. This leads to fewer errors and helps teams focus on their work, making the overall business operations more reliable.
What are the benefits of using LLMs in enterprise settings?
Large Language Models (LLMs) can help businesses by providing insights from data, automating communication, and supporting decision-making. They help save time, improve accuracy, and enhance customer interactions.
Can combining LLMs and Agentic AI really make businesses more predictable?
Yes, when LLMs and Agentic AI work together, they can analyze data and predict outcomes more effectively. This combination allows businesses to anticipate challenges and streamline operations, leading to better predictability in performance.
How can a company get started with these technologies?
To start using LLMs and Agentic AI, businesses should first evaluate their workflows and identify areas for improvement. Then, they can partner with technology providers, train their staff, and gradually integrate these tools into their operations for maximum benefit.
