Business automation is being transformed by Agentic AI. Generative AI (Large Language Models – LLMs) and the Agentic AI pattern together create new ways to solve business problems. As you would expect, 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. Today I’m starting 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 combines a new architectural approach with LLMs to significantly improve business automation. It’s particularly well suited for creating systems with greater autonomy and such systems rely on agents making critical business decisions without human intervention. That said, agents based on LLMs have real limitations of transparency, consistency, state management and regulatory compliance of complex logic. Plus relying on large language models for autonomous decision-making fails to leverage existing investments and best practices in business automation.
Multi-method Agentic AI – an architectural approach that combines agents built with large language models with others based on proven automation technologies including workflow and decision platforms – resolves these limitations and leverages existing investments.
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:
For those who prefer video, check out How AI Agents and Decision Agents Combine Rules & ML in Automation on the IBM Technology channel
For those that prefer to read, download our white paper on Multi-method Agentic AI.
Connect with me here or on LinkedIn if you want to talk about doing this in your own environment.
