Why I built our agentic BI around a rules engine, not a language model

2026-06-30 · Source: Dataconomy · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Intermediate, medium

Summary

A multi-site operations platform successfully implemented an "Agentic BI" system by prioritizing a deterministic rules engine over a language model for core decision-making. Initial attempts placing the language model as the central "brain" failed due to irreproducible and operationally incorrect recommendations. The refined architecture employs a 60% rules engine for decisions, ensuring testability and explainability, and a 40% language model for generating clear, actionable explanations. This system operates directly on the live operational database for real-time decision support. A pilot demonstrated significant operational improvements, including reducing weekly rota preparation from a full day to 90 minutes, increasing actionable event response rates from 40% to 60%, and boosting recommendation acceptance from 41% to 78% through transparency, confidence scores, and visible reasoning. The author advocates for starting with small, single-decision pilots to prove value and build trust.

Key takeaway

For Operations Professionals considering agentic BI systems, prioritize a deterministic rules engine for core decision logic, reserving language models for generating clear explanations. This approach ensures testability, explainability, and builds user trust through transparency, even when the system makes errors. Start with a small, single-decision pilot to validate value and refine rules collaboratively with your team before scaling, mitigating risks associated with complex AI deployments.

Key insights

For Agentic BI, a rules engine should drive decisions, with LLMs providing explanations, ensuring testability and trust.

Principles

• Rules engines ensure testability and explainability in operational decisions.
• LLMs are best for summarization and rephrasing, not final judgment.
• Transparency about system errors builds user trust more than perfection.

Method

Implement a 60% deterministic rules engine for decision-making and a 40% language model for explanation. Run against live operational data. Log all recommendations and outcomes for continuous improvement and transparency.

In practice

• Pilot Agentic BI with a single, clearly defined decision for 6 weeks.
• Involve operations teams directly in defining decision rules.
• Display confidence scores and visible reasoning for all recommendations.

Topics

• Agentic BI
• Rules Engines
• Language Models
• Operational Data
• Decision Support Systems
• System Trust
• Pilot Programs

Best for: Operations Professional, AI Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Dataconomy.