Building Decision Agents with LLMs & Machine Learning Models

Large Language Models (LLMs) are unsuitable for building decision agents in complex AI frameworks due to their inconsistency and lack of transparency. This summary explores an alternative approach using dedicated decision platforms and machine learning models to create consistent, explainable, and agile decision-making systems for enterprise automation.

September 25, 2025 5 min read

Building Decision Agents with LLMs & Machine Learning Models — source: https://www.youtube.com/watch?v=mRkJTXDromw

Decision agents are a crucial component for solving large, complex problems within an agentic AI framework. However, Large Language Models (LLMs), despite being a key technology in agentic AI, are a poor fit for building these specific agents.

Why LLMs Are Not Suited for Decision Agents

LLMs exhibit several characteristics that make them unreliable for critical, autonomous business decisions:

Inconsistency: LLMs can produce different outputs for the same input, which is unacceptable for processes requiring consistent treatment of customers, such as loan applications.
Lack of Transparency: They are “black boxes,” making it difficult to explain why a specific decision was made. This is a significant issue for regulatory compliance and customer communication. When asked to explain themselves, they have been known to fabricate justifications.
Difficulty with Change: While easy to set up initially, altering their behavior in a precise way is challenging. You cannot simply instruct an LLM to stop a specific behavior without retraining or risking unpredictable side effects.
Poor Handling of Structured Data: LLMs excel at processing unstructured text but are not designed to analyze historical, structured data to derive the kind of predictive, analytic insights needed for many business decisions.

The Alternative: Decision Platforms and Business Rules

A more robust solution involves using established automation technology like a Decision Platform or a Business Rules Management System (BRMS). These systems are specifically designed to deliver the necessary capabilities for autonomous decision-making.

Key Benefits of Decision Platforms:

Ruthless Consistency: They execute the same logic the same way every time, ensuring every customer case is evaluated against the same criteria.
Transparency: They provide a formal, auditable definition of the decision logic. It’s possible to log exactly which rules were followed to reach a decision, creating a clear explanation for regulators or customers.
Agility: Decision logic can be updated quickly to respond to changes in regulations, market conditions, or business strategy, without needing to retrain a model.
Low-Code Environment: These platforms often feature low-code editors that empower domain experts—not just programmers—to define and manage the decision logic, ensuring business knowledge is correctly implemented.
Embedded Analytics: They are designed to integrate analytic insights derived from historical data, making decisions more precise and accurate.

Architecture of a Decision Agent System

Building a decision agent requires a combination of prescriptive rules and probabilistic models, managed through a structured platform.

1. Stateless, Side-Effect-Free Agents

Decision agents should be designed to be stateless and side-effect-free.

Stateless: The agent makes a decision based only on the data it receives at that moment. The state is managed externally by a workflow agent, which simplifies the decision logic and improves scalability.
Side-Effect-Free: The agent only makes a decision; it does not take an action. This separation allows the same decision logic (e.g., checking eligibility) to be reused across multiple business processes that might trigger different actions.

2. Components of a Decision Platform

A typical decision platform includes:

Editors: Both technical IDEs and low-code editors for authoring business rules and decision logic.
Repository: A centralized, version-controlled repository to manage decision assets, supporting branching and collaborative development.
Validation & Testing Tools: Tools to check for logical completeness, overlapping rules, and other errors. Robust test suites allow for automated testing against large datasets.
Simulation & Impact Analysis: Engines that run simulations on historical data to show the business impact of a proposed rule change before it is deployed.
Deployment Engine: A component that deploys the decision logic as a service (e.g., a REST endpoint), which can then be wrapped and exposed as an agent within the AI framework.

Incorporating Machine Learning for Probabilistic Decisions

Many decisions, like loan origination, include probabilistic elements such as fraud likelihood, credit risk, or payoff risk. These are handled by machine learning models.

Model Development: Using a machine learning platform, data scientists analyze historical data, perform feature engineering, and train models (e.g., neural networks, regression models) to make predictions.
Deployment as Analytic Agents: These trained models are deployed as endpoints. Like decision services, these endpoints can be wrapped as “analytic agents” that provide scores or predictions.
Integration: The primary decision agent consumes the outputs from these analytic agents. For example, a loan origination decision rule might use the credit risk score from an analytic agent as one of its inputs.

The Synergistic Role of LLMs

While not suitable for the core decision logic, LLMs can enhance the system in two key areas:

Data Ingestion: LLMs are excellent at extracting structured data from unstructured sources like documents, brochures, or conversation transcripts. This data can then be fed as input into the decision agent.
Result Explanation: The detailed, technical log produced by a decision agent can be fed to an LLM to generate a clear, natural-language explanation for a customer or a service representative.

How Decision Systems Learn and Improve

Improvement mechanisms differ for analytic and decision agents.

Analytic Agents: Unsupervised models can learn continuously as new data arrives. Supervised models are typically retrained periodically by data scientists to ensure they remain accurate.
Decision Agents: These agents don’t “learn” autonomously, as their behavior is meant to be explicit and controlled. Improvement is achieved through structured experimentation, such as A/B testing (or champion/challenger testing), where different rule versions are run in parallel to see which performs better.
Framework-Level Learning: Improving the overall business outcome (e.g., making better loan decisions) requires a long-term feedback loop. Decisions and their inputs must be logged, and the actual outcomes (e.g., loan performance over years) must be analyzed later to refine both the decision rules and the underlying predictive models. This requires a systematic, holistic approach to learning owned by the business.