Agentic Reinforced and Operational Workflow

• Author(s): Ayush Maurya ; Deependra Bahadur Maurya
• Paper ID: 1710430
• Page: 221-229
• Published Date: 05-09-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 3 September-2025

Abstract

We present Agentic Reinforced and Operational Workflow (AROW), a novel multi-agent system that integrates large pretrained language models (PLMs) with cooperative multi-agent reinforcement learning (MARL) to perform complex tasks with improved coordination and factual reliability. The system features a Neural Execution Planner (NEP) that parses a user query into subgoals, a decentralized Reinforcement Distributor to allocate credit, and a PLM-based supervisor that assigns subtasks to specialized agents via JSON-formatted instructions. Agents communicate through a shared-memory “blackboard” for intermediate results. During execution, each agent’s output is validated (e.g. by verifier agents and RAG grounding) and assigned a quality score $x_i\in{0,1}$ for reinforcement. Learning employs cooperative MARL techniques: we use QMIX’s monotonic value-mixing network to learn a global action-value and COMA’s counterfactual baseline for credit assignment[1][2]. For hallucination mitigation, outputs are constrained by strict JSON schema (enforced via prompt priming[3]), cross-checked against retrieved documents (RAG), and subject to provenance tracking and reward penalties for unverifiable claims[4][5]. We evaluate AROW on two fronts: (1) synthetic cooperative simulations (e.g. multi-robot resource-gathering tasks[6]) to measure coordination and credit learning, and (2) document-grounded QA challenges to test fact-consistency. Example JSON instructions, agent responses, and verifier behavior are provided. Results (theoretical) indicate enhanced task performance and reduced hallucinations compared to baselines. The paper emphasizes the practical integration of agentic architectures with MARL to achieve scalable, reliable autonomous workflows.

Citations

IRE Journals:
Ayush Maurya , Deependra Bahadur Maurya "Agentic Reinforced and Operational Workflow" Iconic Research And Engineering Journals Volume 9 Issue 3 2025 Page 221-229

IEEE:
Ayush Maurya , Deependra Bahadur Maurya "Agentic Reinforced and Operational Workflow" Iconic Research And Engineering Journals, 9(3)