# Agentic AI Sustainability Assessment for Supply Chain Document Insights

arXiv: https://arxiv.org/abs/2511.07097 · HTML: https://mlsanity.com/paper/2511.07097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 11 of 11)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply Chains](https://mlsanity.com/paper/2407.11384/) (2024)
- [Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworks](https://mlsanity.com/paper/2503.11517/) (2025)
- [Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems](https://mlsanity.com/paper/2509.14956/) (2025)
- [AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations](https://mlsanity.com/paper/2411.05828/) (2024)
- [Smart Audit System Empowered by LLM](https://mlsanity.com/paper/2410.07677/) (2024)
- [Rethinking Supply Chain Planning: A Generative Paradigm](https://mlsanity.com/paper/2509.03811/) (2025)
- [Generating Plans for Belief-Desire-Intention (BDI) Agents Using Alternating-Time Temporal Logic (ATL)](https://mlsanity.com/paper/2509.15238/) (2025)
- [Semantic Intelligence: A Bio-Inspired Cognitive Framework for Embodied Agents](https://mlsanity.com/paper/2510.17129/) (2025)

## Built on by (most cited first, 2 of 2)

- [Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching](https://mlsanity.com/paper/2601.13186/) (2026)
- [From SQL Generation to Tool Selection: A Domain-Oriented Pattern for MCP Servers](https://mlsanity.com/paper/2608.22063/) (2026)

## Abstract (arXiv)

This paper presents a comprehensive sustainability assessment framework for document intelligence within supply chain operations, centered on agentic artificial intelligence (AI). We address the dual objective of improving automation efficiency while providing measurable environmental performance in document-intensive workflows. The research compares three scenarios: fully manual (human-only), AI-assisted (human-in-the-loop, HITL), and an advanced multi-agent agentic AI workflow leveraging parsers and verifiers. Empirical results show that AI-assisted HITL and agentic AI scenarios achieve reductions of up to 70-90% in energy consumption, 90-97% in carbon dioxide emissions, and 89-98% in water usage compared to manual processes. Notably, full agentic configurations, combining advanced reasoning (thinking mode) and multi-agent validation, achieve substantial sustainability gains over human-only approaches, even when resource usage increases slightly versus simpler AI-assisted solutions. The framework integrates performance, energy, and emission indicators into a unified ESG-oriented methodology for assessing and governing AI-enabled supply chain solutions. The paper includes a complete replicability use case demonstrating the methodology's application to real-world document extraction tasks.