# Organizational Memory for Agentic Business Process Execution

arXiv: https://arxiv.org/abs/2607.03228 · HTML: https://mlsanity.com/paper/2607.03228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://mlsanity.com/paper/2305.10250/) (2023)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [SCM: Enhancing Large Language Model with Self-Controlled Memory Framework](https://mlsanity.com/paper/2304.13343/) (2023)
- [Agentic Business Process Management: Practitioner Perspectives on Agent Governance in Business Processes](https://mlsanity.com/paper/2504.03693/) (2025)
- [Agent Miner: An Algorithm for Discovering Agent Systems from Event Data](https://mlsanity.com/paper/2212.01454/) (2022)
- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [SmartFlow: Robotic Process Automation using LLMs](https://mlsanity.com/paper/2405.12842/) (2024)
- [Agentic Business Process Management Systems](https://mlsanity.com/paper/2601.18833/) (2026)
- [Formal Foundations of Agentic Business Process Management](https://mlsanity.com/paper/2604.17347/) (2026)
- [From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover Policies](https://mlsanity.com/paper/2606.01857/) (2026)

## Built on by (most cited first, 1 of 1)

- [Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents](https://mlsanity.com/paper/2610.02002/) (2026)

## Abstract (arXiv)

LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLMs lack the organization-specific knowledge required for reliable execution, which is typically fragmented across human-oriented artifacts such as policies, process models, and standard operating procedures. While such knowledge can technically be encoded in individual prompts or agent-specific retrieval setups, this approach does not scale in enterprises, as it gives rise to knowledge silos and rule duplicates, and makes consistent updates and learning across agents difficult. We argue that this calls for an organizational memory for agentic business process execution: a shared, governed, and agent-consumable reference layer of evolving organization-specific procedural knowledge about how work should be executed. We derive requirements for such a memory, propose an architecture for its curation and consumption, and demonstrate its effectiveness in a proof-of-concept based on a procurement scenario.