Sheikh Nazib Ahmed | Searcharxiv

arXiv2026

AgentModernize: Preserving Business Logic in Legacy Modernization with Multi-Agent LLMs and Behavioral Specification Graphs

Legacy modernization breaks business logic more often than most teams expect. Most tools and LLM-based approaches treat modernization as syntax translation: convert COBOL to Java, swap PL/SQL for Python, ship it. Implicit rules, edge-case handling, and cross-module constraints that keep production systems running are lost, and nobody notices until something fails in production. We present AgentModernize, a multi-agent framework that treats modernization as a behavioral preservation problem. Four agents handle extraction, specification, code generation, and validation. The key intermediate artifact, a Behavioral Specification Graph (BSG), forces extracted business logic to be explicit and inspectable before any code is generated. We evaluated on LegacyModernize-8, eight synthetic scenarios spanning telecom and banking, under a fair protocol where each method's tests are generated from its own API surface (3 trials, temperature 0.0). With GPT-4o-mini, AgentModernize with feedback achieves 23.0% mean BER (non-zero on 5/8 scenarios, up to 53.3%), while AgentModernize without feedback reaches 23.8%. SP-LLM scores 12.4% (non-zero on 2/8) and CoT-LLM 4.5% (2/8). No single method dominates all scenarios. The feedback loop is decisive for scenarios requiring iterative correction (S4, S6, S7) but can regress code in others (S2, S8). The BSG captures 92.3% of gold-standard rules with 90.2% precision; the bottleneck is code generation, not extraction. A cross-model study (GPT-4o, GPT-5.3-codex) reveals the pipeline's benefit is inversely correlated with model capability: stronger models achieve higher BER with single-prompt methods than with the pipeline. For regulated industries, the pipeline's traceable artifacts (business rule inventory, BSG, equivalence reports) provide an audit trail that no single-prompt approach can match.

cs.SE↗

arXiv2026

Beyond Document Retrieval: Architectural Challenges When LLM Agents Query Structured Enterprise Data

Retrieval-augmented generation (RAG) has become a common architecture for connecting large language models to enterprise knowledge. Most RAG systems retrieve unstructured documents (PDFs, wiki pages, support tickets) and feed them to an LLM for summarization or question answering. A growing class of enterprise agents, however, must query structured data: relational databases, data warehouses, and analytics APIs where the answer is a computed result, not a retrieved passage. Structured-data querying forces decisions that a document-RAG pipeline never has to make. We group them into seven dimensions: retrieval semantics, authorization, intent recognition, entity resolution, evaluation, failure modes, and latency. For each dimension, we characterize the baseline assumption, explain its limitation for structured data, and describe a generic architectural pattern. As supporting evidence, a controlled synthetic study shows that a staged agent built on this framework eliminates the authorization violations of a direct translate-and-execute baseline under controlled conditions. The primary result is a design-oriented framework, an evaluation protocol, and a set of open problems for governed structured-data agents.

cs.DL↗