Business automation is being transformed by Agentic AI. The combination of generative AI or Large Language Models (LLMs) and the Agentic AI pattern creates an opportunity to solve business problems in new ways. I am particularly interested in what you might call decision agents – agents in an agentic framework specifically focused on automating business decisions. How do you identify, integrate, specify, design and build such decision agents and what technology and design approaches do you need?
As part of thinking about this topic, I recorded three lightboard videos for the IBM Technology YouTube channel and blogged about them here. As promised, though delayed until after the holiday, here are some longer pieces on the three topics.
- How AI Agents and Decision Agents Combine Rules & ML in Automation [this post]
- Building Decision Agents with LLMs & Machine Learning Models
- Designing AI Decision Agents with DMN, Machine Learning & Analytics
Agentic AI is a new and powerful pattern in business automation. Combining a new architectural approach with large language or generative AI models it offers potential for significant improvements in the scope, effectiveness and adaptability of business automation. Agentic AI is particularly well suited for creating systems with greater autonomy and such systems rely on agents making critical business decisions without human intervention.
However, agents based on generative AI have real limitations of transparency, consistency, state management and regulatory compliance of complex logic. In addition, relying on large language models for autonomous decision-making fails to leverage existing investments and best practices in business automation.
To resolve these limitations, and leverage existing investments, you need to adopt multi-method Agentic AI. This is an architectural approach that combines agents built with large language models with others based on proven automation technologies including workflow and decision platforms, as well as predictive analytic models.
A multi-method approach divides agents into different types or classifications, applying the right technology (or mix of technologies for each). For instance:
- Chat agents for natural language interactions use generative AI to handle questions and requests from users.
- Orchestration agents use generative AI to route interpreted requests to the right specialty agent.
- Policy agents use Retrieval Augmented Generation (RAG) and generative AI to answer a wide range of general questions.
- Workflow agents use process management technology to handle complex sequences of steps and manage state.
- Decision agents deliver consistent and explainable decisions using decision platforms and business rules.
- Document ingestion agents use generative AI to extract needed information from free from documents.
- Explainer agents translate decisions made by decision agents into natural language for customers and staff.
- Companion agents use generative AI to support staff as they handle manual steps and reviews.
While many agents in such a framework do use generative AI, not all do. Such a multi-method approach improves confidence and transparency, leverages existing technology investments AND puts generative AI to work effectively. It combines the best tools for each task, maintains transparency where it matters, and builds systems that serve both customers and stakeholders with excellence.
To learn more, here are two options:
Connect with me here or on LinkedIn if you want to talk about doing this in your own environment.
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