AgentifyBench is a benchmark that evaluates AI agents' ability to extract and map legal entities to Customer Relationship Management (CRM) ontology structures across multiple conversation turns. Unlike existing single-turn benchmarks, AgentifyBench tests three critical dimensions: (1) Entity Extraction Accuracy: Can agents identify correct entity types and names from legal text? (2) Relationship Mapping Precision: Can agents establish correct semantic relationships between entities? (3) Multi-Turn Consistency: Do agents maintain semantic understanding when presented with corrections or new information? At the time of submission(we will be expanding the data), the benchmark comprises three episodes across distinct legal domains: construction defects, employment discrimination, and commercial contract breaches. Each episode contains three conversation turns with gold-standard CRM ontology definitions. Agents are evaluated using objective F1-based metrics (Entity F1, Relationship F1) and a novel Persistence metric measuring relationship stability across turns. AgentifyBench addresses a real-world problem: legal teams need agents that can auto-populate CRM systems from unstructured documents while maintaining consistency as information evolves. Current evaluation methods don't measure this capability. At the time of submission, benchmark achieves 0.462 Entity F1 and 0.347 Relationship F1 on a Gemini 2.5 Flash baseline, demonstrating meaningful room for improvement while revealing specific failure modes in relationship type classification.