Business automation is being transformed by Agentic AI. As organizations adopt generative AI and the agentic AI pattern, new ways to effectively automate business problems are emerging. One area of particular interest to me is how you identify, integrate, design and build decision agents in an agentic framework and the technology and design approaches you need to do that.
To address this, I recorded three lightboard videos for the IBM Technology YouTube channel.
The first is all about the role of Decision Agents in Agentic AI - How AI Agents and Decision Agents Combine Rules & ML in Automation. This takes an agentic AI approach to solving a well-known business problem – loan origination – and shows how using Decision Agents can deliver efficient, consistent decision-making in an agentic framework by effectively applying business rules and machine learning.
The second drills into Decision Agents - Building Decision Agents with LLMs & Machine Learning Models. This talks about how decision agents complement LLMs and how you can design and build decision agents that use LLMs, machine learning models and business rules to enable scalable decision-making in AI frameworks. Decision Agents can leverage any of IBM's Decisions technology - IBM Operational Decision Manager (ODM), Automation Decision Services (ADS), Decision Manager Open Edition (DMOE) or the new Decision Intelligence.
Finally, I show you how to actually design these Decision Agents visually using the Decision Model and Notation or DMN standard - Designing AI Decision Agents with DMN, Machine Learning & Analytics. This shows how DMN lets you think through the behavior you need in a decision agent and design an agent that leverages the right mix of technologies for a successful agentic AI implementation. I regularly talk about decision modeling and the Decision Model and Notation (DMN) standard - you can find a whole series of posts on this topic here.
Next week I’ll start a series of longer posts, drilling into each of these.
Connect with me here or on LinkedIn if you want to talk about doing this in your own environment.
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