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Agentic Reinforced and Operational Workflow
Subject area: Science,Engineering and Technology · Area of research: Artificial intelligence
DOI: 10.64388/IREV9I2-1710430-859
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.
References
[1] Foerster et al., Counterfactual Multi-Agent Policy Gradients (COMA)[2].
[2] Rashid et al., QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent RL[1].
[3] Yu and McQuade, RAG-KG-IL: A Multi-Agent Framework for Reducing LLM Hallucinations[4].
[4] Sankararaman et al., Provenance: A Light-weight Fact-checker for RAG LLM Output[5].
[5] Arnes & Horsch, Schema-Based Priming of LLM for Data Validation[3].
[6] Han & Zhang, LLM Multi-Agent Systems Based on Blackboard Architecture[8].
[7] Anthropic, How We Built Our Multi-Agent Research System[7].
[8] Azadeh et al., Advances in MARL: Persistent Autonomy and Robot Learning Lab[6].
[9] [1803.11485] QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learninghttps://arxiv.org/abs/1803.11485
[10] [1705.08926] Counterfactual Multi-Agent PolicyGradientshttps://arxiv.org/abs/1705.08926
[11] Schema-Based Priming of Large Language Model for Data Object Validation Compliance by Jo Inge Arnes, Alexander Horsch :: SSRNhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4453361
[12] [2503.13514] RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integrationhttps://arxiv.org/abs/2503.13514
[13] Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Outputhttps://arxiv.org/html/2411.01022v1
[14] (a) multi-agent grid-world environment with four agents (circles) and... | Download Scientific Diagramhttps://www.researchgate.net/figure/a-multi-agent-grid-world-environment-with-four-agentscirclesandfourresources_fig1_387539902
[15] How we built our multi-agent research system \ Anthropichttps://www.anthropic.com/engineering/multi-agent-research-system
[16] [2507.01701] Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecturehttps://arxiv.org/abs/2507.01701
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[18] Preventing LLM Hallucinations: A Technical GuideAetherLabBlohttps://aetherlab.co/blog/preventing-llm-hallucinations-guide
How to cite this paper
@article{1710430,
author = {Ayush Maurya, Deependra Bahadur Maurya},
title = {Agentic Reinforced and Operational Workflow},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {221-229},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710430.pdf},
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.},
month = {September},
doi = {https://doi.org/10.64388/IREV9I2-1710430-859}
}