BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning

Merkle has developed BADGER, a unified evaluation framework that combines text-to-SQL assessment with agentic behavior evaluation for enterprise AI systems. The framework achieves substantial agreement with human expert judgment (Cohen's kappa=0.717) and outperforms six competing evaluation approaches, addressing a critical gap in production-grade AI system assessment.

BADGER represents a significant advancement in enterprise AI evaluation methodology, addressing a well-documented gap between academic benchmarking and real-world production requirements. Traditional evaluation frameworks like Spider and BIRD focus primarily on execution accuracy for SQL generation, while newer approaches like G-Eval and RAGAS assess LLM outputs through proxy metrics. BADGER uniquely bridges these domains by integrating deterministic SQL validation with agentic behavior assessment, recognizing that modern enterprise systems increasingly rely on multi-step reasoning chains rather than single-query execution.

The framework's technical innovations directly address practical brittleness in existing systems. Column aliasing and numeric tolerance issues have long plagued deterministic scoring approaches, causing false negatives that don't reflect actual system utility. By leveraging LLM-assisted structural inference before cell-level comparison, Hybrid-EX achieves 87.3% balanced accuracy against human annotation—a substantial improvement over competing frameworks with effect sizes demonstrating statistical significance (p≤0.001). This represents genuine methodological progress rather than incremental refinement.

For the broader AI infrastructure ecosystem, BADGER's adoption could standardize enterprise evaluation practices, reducing friction in deploying agentic systems to regulated industries. The emphasis on client-governed data environments and configurable LLM judge backends addresses critical compliance concerns that have historically limited AI system adoption in enterprise settings. Organizations currently wrestling with how to validate text-to-SQL and agentic systems face concrete pressure to adopt standardized assessment protocols.

The framework's practical applicability extends beyond SQL generation to any enterprise reasoning pipeline, positioning evaluation methodology itself as a competitive advantage. Teams that systematize evaluation early gain visibility into system degradation and can iterate more rapidly than competitors relying on ad-hoc quality gates.

• →BADGER achieves Cohen's kappa=0.717 for agreement with human expert judgment, substantially outperforming six competing evaluation frameworks.
• →Hybrid-EX execution accuracy metric resolves column-aliasing and numeric-tolerance issues through LLM-assisted structural inference before deterministic scoring.
• →Framework integrates text-to-SQL assessment with agentic behavior evaluation into unified pipeline, addressing critical gap in enterprise AI system validation.
• →Client-governed data environment and configurable LLM backends enable compliance-aligned deployment in regulated industries.
• →Novel 'Excess Tool Usage' metric specifically targets agentic behavior assessment rather than relying solely on proxy LLM-based metrics.