{
  "id": 5109,
  "url": "https://arxiv.org/abs/2605.12532v1",
  "title": "AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems",
  "summary": "Conventional algorithmic trading systems are grounded in deterministic heuristics or offline-trained statistical models that cannot adapt to the semantic complexity of rapidly shifting market regimes. This paper introduces AGENTICAITA, an agentic AI framework that replaces the traditional signal then execute paradigm with a fully autonomous deliberative loop in which multiple specialized Large Language Model agents reason, negotiate, and act in concert - without any offline training or human int",
  "authors": "Ivan Letteri",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-01T16:25:43.000Z",
  "fetched_at": "2026-07-14T16:31:31.211Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5109",
  "original_url": "https://arxiv.org/abs/2605.12532v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}