Enterprise AI Benchmarking: Rule-Based vs LLM vs Hybrid Agents

In a rapidly digitalizing enterprise landscape, the ability to compare different AI agent architectures—rule‑based, large‑language‑model (LLM) powered, and hybrid—on real‑world tasks is essential. The tutorial presents a coding implementation of a comprehensive benchmarking framework that rigorously evaluates these agents across a suite of enterprise software challenges. By embedding the framework in a Python environment, researchers and practitioners can reuse, extend, and adapt the code base to suit specific business contexts. The framework’s modular design allows for plug‑in new tasks or agents without rewriting core logic.

The benchmark suite covers five core categories: data transformation, API integration, workflow orchestration, performance tuning, and anomaly detection. Each category includes multiple sub‑tasks, such as CSV‑to‑SQL conversion, REST‑API data ingestion, multi‑step approval pipelines, cache‑coherence optimization, and outlier identification in log streams. Evaluation metrics span accuracy, execution time, resource consumption, and human‑readability of the agent’s decision logs. The tutorial walks through the creation of synthetic datasets, the registration of agents, and the automated collection of metrics, culminating in a visual dashboard that juxtaposes agent performance across all tasks.

Results from the tutorial demonstrate that rule‑based agents excel in deterministic, low‑variance scenarios but struggle with ambiguous input. LLM agents show superior flexibility, often generating correct solutions for unforeseen edge cases, yet they incur higher latency and token costs. Hybrid agents—combining a rule engine with LLM inference—strike a balance, delivering near‑rule‑based speed while maintaining LLM adaptability. The authors conclude that enterprises should adopt a hybrid strategy for mission‑critical workflows, reserving pure LLM agents for exploratory data analysis and rule‑based agents for compliance‑bound transformations. The open‑source implementation invites the community to contribute new tasks, agents, and metrics, paving the way for a standardized benchmark that can guide AI adoption decisions.