Wrapping up my series on decision agents, here’s the third post.
- How AI Agents and Decision Agents Combine Rules & ML in Automation
- Building Decision Agents with LLMs & Machine Learning Models
- Designing AI Decision Agents with DMN, Machine Learning & Analytics [this post]
The most effective way to define decision agents is using decision modeling. Just as you build a data model for a database or a process model for workflow, a decision model lets you create a visual blueprint for your decision agents. We use the industry standard notation for decision models - the Decision Model and Notation or DMN. This is partly because it’s a standard and partly because the basics of DMN are incredibly simple – three shapes and two lines. Yet this simplicity supports enormously complex decision agent designs.
I wrote a whole series of posts on this decision modeling here, so I won’t repeat myself. Suffice it to say you can model out enormously complex decision agents, breaking down their decision-making into its component pieces and then specifying the logic for any piece that must be consistent and prescriptive while identifying the right kind of machine learning model for probabilistic decisions such as determining the sentiment of a text field for instance.
The prescriptive decisions in such a model can easily be implemented using a decision platform while the others can be executed on AI/ML models and even specified using standards such as PMML – Predictive Model Markup Language, an XML standard for interchanging predictive models or ONNX, Open Neural Network Exchange, which exchanges graph models between different ML platforms.
Each decision agent can leverage one or more decision services defined this way using MCP to communicate with the stateless services.
Besides integrating LLMs into the model for execution, you can also use them to help you build the models. While it is quick to build these by hand–10x faster than writing requirements documents, LLMs can accelerate this even further by taking your policy documents, Standard Operating Procedures and regulations and extracting initial partial decision models from them. These won’t be 100% right because most organizations don’t have everything documented but they will accelerate your process. You can also use LLMs trained on programming languages to extract models from code.
The DMN model represents a precise, visual definition of your decision-making that matches the behavior of your decision agents. This allows you to easily track changes, mix and match the right technology for each agent, engage business owners in the definition of your agents and produce regulatory documentation of how you decided. All things that are REALLY hard to do any other way.
To learn more about decision modeling with DMN, there are several options:
Connect with me here or on LinkedIn if you want to talk about doing this in your own environment.
#IBMChampions #IBMChampion #DMN #DecisionManagement #DecisionManagerOpenEdition #ML #AI #GenerativeAI #LLM #DecisionAutomation