Enterprise ai frameworks vary by application and component role
Comparative Framework Analysis for Enterprise Generative AI Applications: Chatbot, Automation, and Oracle-to-PostgreSQL Migration
Software EngineeringLogic in Computer ScienceMultiagent Systems
Summary
Different enterprise applications using generative AI—such as chatbots, email automation, and database migration—require different software frameworks. The authors compare frameworks based on how they divide tasks between uncertain AI parts and definite rule-based parts. They find that choosing the best framework depends on the specific application and what each component must do. This means there is no one best framework for all uses, and developers should pick frameworks per component with clear rules and testing.
What this means in practice
- •For enterprise software developers: Develop component-based AI applications that combine proposal-generating AI with rule-based decision systems for tasks like documentation assistance and email automation.
- •For database migration engineers: Implement layered tools that separate AI-based suggestion from strict validation and routing to support complex data migrations such as Oracle to PostgreSQL.
Authors
Oleg Grynets, Olena Pochernina, Alona Seletska, Daryna Tukalo, Dmytro Kostetskyi, Ivan Fedorchuk, Vasyl Lyashkevych
Abstract
This study compares framework suitability across three classes of enterprise generative AI applications: a documentation-based development assistant, an email and inquiry automation system, and an Oracle-to-PostgreSQL migration tool. The analysis evaluates component boundaries, orchestration, policy retrieval or reasoning, model integration, deterministic validation, persistence, observability, and operational efficiency. Across the three applications, the evidence supports layered architectures in which probabilistic components generate proposals, while deterministic components retain authority over routing, authorization, validation, persistence, idempotency, and final outcomes. The results indicate that framework suitability depends on the application, deployment conditions, and component responsibility: search quality, workflow control, safety behavior, and migration validation cannot be reduced to a single cross-application assessment. Therefore, the study substantiates the need for framework selection at the component level, supported by explicit contracts, application-specific evidence, and independent validation boundaries.