Agentic Code Review: A Multi-Agent Framework with Meta-Cognitive Reflection and Human-in-The-Loop Alignment

Automated code review remains a persistent challenge in software engineering. Traditional static analysis tools depend on rigid pattern matching and produce excessive false positives, while large language model (LLM)-based approaches frequently hallucinate findings that reference nonexistent code. This paper presents a multi-Agent framework that addresses both limitations through orchestrated specialization and adversarial self-verification. The proposed system deploys four agents via LangGraph: A security specialist that combines Bandit static analysis with LLM-based contextual reasoning, a retrievalaugmented performance optimizer that grounds suggestions in historical pull request data, a meta-cognitive critic that validates all findings against the original source code to filter hallucinations, and a lead architect that synthesizes only verified results. A human-in-The-loop checkpoint ensures that critical decisions remain under expert oversight before final synthesis. Evaluation on deliberately vulnerable Python code samples demonstrates that the critic agent reduces false positives by 40% relative to a single-Agent baseline, improves line-number accuracy from 67% to 9 2%, and lowers the hallucination rate from 3 2% to 1 8%. An ablation study confirms that the meta-cognitive verification layer contributes most significantly to these gains. These results indicate that adversarial self-verification within multi-Agent architectures can meaningfully improve the reliability of LLMdriven code analysis for high-stakes software review tasks.