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Author

G. Nizhnichenko

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2026

MODEL AND AGENT EVALUATION SYSTEM IN A MULTI-AGENT INTELLIGENT COMPETITIVE INTELLIGENCE PLATFORM: ARCHITECTURE AND METHODOLOGY

The article addresses the problem of evaluating the quality of large language models (LLMs) and intelligent agents in multi-agent competitive intelligence automation platforms. The aim is to develop the architecture of a two-level evaluation system featuring an Evaluator Agent with reasoning chain tracing, a control dataset of 30+ use cases, and a validation mechanism on samples of real user queries. The proposed architecture includes Cell Agent, Cell Agent Models, Evaluator Agent, and a results database. A four-criteria verification model is introduced: (a) step verifiability and logical consistency, (b) source correctness and relevance, (c) result correctness, and (d) solution path optimality. The scientific novelty lies in substantiating an integrated evaluation approach unifying model benchmarking and agent tracing within a single architecture.

G. Nizhnichenko · 0 citations
2026

ARCHITECTURE OF A VALIDATION ORCHESTRATOR IN A MULTI-AGENT INTELLIGENT COMPETITIVE INTELLIGENCE PLATFORM

The article addresses the problem of automated validation of bibliographic and factual sources in the context of multi-agent intelligent competitive intelligence platforms. The relevance of the study is driven by the growing volume of information flows and the need to ensure the reliability of data used for management decision-making in organizational systems. The aim of the work is to develop the architecture of a validation orchestrator that provides hybrid routing of requests between APIs of scientific databases and web resource parsing modules. A multi-level orchestrator architecture is proposed, including a routing layer, a unified evidence schema, a cross-verification mechanism with a four-level reliability scale (Confirmed, Likely, Unknown, Conflict), and a request audit subsystem. A comparative analysis of API-based and parsing-based approaches is conducted against criteria of stability, source coverage, reproducibility, and completeness of extracted data. The scientific novelty lies in substantiating a hybrid approach to validation with a formalized evidence aggregation schema from heterogeneous sources.

G. Nizhnichenko · 0 citations