AGENTIC-SYSTEMSJuly 7, 20261 min read

Organizational Memory for Agentic Business Process Execution

Researchers propose 'Organizational Memory' to address the lack of organization-specific knowledge in LLM-based agents, offering a shared, governed reference layer to scale automated business process execution.

Computer Science > Artificial Intelligence

Title:Organizational Memory for Agentic Business Process Execution

View PDF HTML (experimental)Abstract: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.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

Stay in the loop

Get the best AI & tech insights in your inbox — no spam, unsubscribe anytime.

More in this category

agentic-systems

Automated Data Readiness for Scientific AI

Researchers have introduced REDI, an open-source framework that automates the transformation of raw scientific datasets into AI-ready data. Tested on the Frontier supercomputer, REDI addresses the data preparation bottleneck in scientific AI domains.

agentic-systems

ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability

Researchers have introduced ASK+, a novel framework that significantly enhances small language model (SLM) assistance for reinforcement learning agents under partial observability. By providing trajectory-aware context and structured reasoning, ASK+ demonstrates that prompt design and selective gating can outperform sheer model scale.

agentic-systems

When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

Evaluating agentic policy repair without per-state expert labels is challenging, as aggregate behavioral alignment metrics can be highly misleading. A new study using a hotel-pricing simulator demonstrates that LLM-based editors can achieve near-benchmark revenue by leveraging indirect diagnostic feedback, highlighting the need for closed-loop evaluation.

agentic-systems

Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems

A new study proposes 'embodied operators' to modularize embodied intelligence systems, enabling reusable and deployable robot pipelines beyond end-to-end models, supported by a multi-dimensional benchmarking framework.

agentic-systems

Evaluating Generative Agents with Actions Grounded in Socially Distributed Task Environments using Incognita

Researchers introduce Incognita, a framework to evaluate generative agents in socially distributed task environments where knowledge is partitioned across multiple entities. Testing shows that while advanced models perform better at gathering hidden information, overall reliability in executing grounded actions remains low.

agentic-systems

Object-Centric Environment Modeling for Agentic Tasks

Researchers propose Object-Centric Environment Modeling (OCM) to help AI agents organize accumulated experiences into an executable programmatic model, addressing the limitations of traditional free-form textual memories.