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Building Decision Agents with LLMs & Machine Learning Models

By James Taylor posted Fri January 09, 2026 02:44 PM

  

Continuing my series on decision agents, here’s the second post.

  1. How AI Agents and Decision Agents Combine Rules & ML in Automation
  2. Building Decision Agents with LLMs & Machine Learning Models [this post]
  3. Designing AI Decision Agents with DMN, Machine Learning & Analytics

Decision agents are essential components in any sophisticated agentic AI system designed to solve large, complex problems. These agents must make autonomous decisions that directly impact customers, operations, and business outcomes. However, the technology that powers modern agentic AI—large language models—creates a fundamental paradox: while LLMs excel at many tasks, they are poorly suited for the precise, consistent, and transparent decision-making that business-critical applications require.

Generative AI models, LLMs, are unsuitable for advanced decision-making for several reasons.

  • They are inconsistent by design, not something we look for in decision-making
  • They are opaque and black-boxy, making it hard to explain why a decision was made
  • They are poor at mathematical analysis and much worse than other machine learning techniques
  • It’s hard to make small, focused changes to their behavior, limiting continuous business-driven improvement

In contrast, a good decision agent should be ruthlessly consistent, completely transparent, easy to change, accessible to domain experts and able to embed advanced analytics and machine learning.

Fortunately, platforms that meet these criteria – Decision Platforms or Business Rules Management Systems – are widely used and are ideal for building decision agents. For instance, 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.

Plus, these platforms can be enhanced with generative AI by using it to ingest unstructured information, explain decisions made in natural language and suggest improvements.

To learn more about decision agents, here are two options:

Connect with me here or on LinkedIn if you want to talk about doing this in your own environment.

#IBMChampions#IBMChampion #OperationalDecisionManager #IBMOperationalDecisionManager #AutomationDecisionServices(ADS) #DecisionManagerOpenEdition #DecisionManagement #AI #watsonxorchestrate #watsonx.ai

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Comments

Suman Suhag
Mon January 12, 2026 06:04 AM

Hi Dear Sir,

Architecture truth:





Agentic AI → LLM (unstructured data → context) 
          ↓
Decision Agent → ODM/ADS (rules + ML → decision) 
          ↓  
Execution → Transparent, auditable, changeable

LLM failure modes eliminated:

  • Inconsistency → Rules execute identically every time

  • Opacity → Decision traces show exact rule path

  • Math weakness → XGBoost/PMML models embedded

  • Change resistance → Business authors modify via Excel

Production hybrid: watsonx.ai ingests docs, ODM decision service executes natural language explanation generated. 

Million decisions/hour, 100% explainable.

Taylor's stack = enterprise reality: LLMs for context, Decision Platforms for execution. Deploy the ODM microservice. Decision intelligence perfected.