Organizational Memory for Agentic Business Process Execution

• URL: http://arxiv.org/abs/2607.03228v1
• Date: Fri, 03 Jul 2026 11:40:10 GMT
• Title: Organizational Memory for Agentic Business Process Execution
• Abstract summary: General-purpose LLMs lack the organization-specific knowledge required for reliable execution.<n>This paper proposes an organizational memory for agentic business process execution.
• Score: 0.7080990243618377
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Formal Verification of Agentic Systems over Operational Data [59.98246281888422]
  Agentic systems driven by large language models (LLMs) are increasingly deployed in real-world where they act on persistent operational data.<n>Existing approaches do not provide such system-level guarantees.<n>We study here the verification of agentic systems comprising a single LLM and a tool orchestration harness over relational operational data.
  arXiv  Detail & Related papers  (2026-08-04T13:01:30Z)
• Knowledge-Centric Agents for Workflow Generation [73.4340391112949]
  We propose a knowledge-centric framework that learns to invert, inject, and infer with knowledge across multiple abstraction levels.<n>Our method produces with richer node diversity, more coherent structures, and higher execution success rates than existing systems.
  arXiv  Detail & Related papers  (2026-07-17T11:03:01Z)
• Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution [0.40631409309544836]
  Large Language Models (LLMs) have accelerated the adoption of software development agents, now widely available as Integrated Development Environment (IDE) extensions and standalone applications.<n>While these agents are typically general-purpose, it remains unclear whether specialist agents justify their additional development effort.<n>We investigate this question in the context of business process automation, focusing on the transformation of Business Process Model and Notation (BPMN) diagrams into executable agentic.
  arXiv  Detail & Related papers  (2026-07-16T01:06:38Z)
• A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO [2.0840789905678485]
  We introduce the process harness, a new mechanism for uplifting legacy into Agentic Business Process Management (Agentic BPM)<n>A process harness places a policy-governed agentic layer around a deterministic workflow engine, intercepting designated control points to contribute reasoning, adaptation, and oversight while the engine retains structural authority over the process.<n>We present CUGA FLO as the design and implementation realization of the TDF model, and demonstrate it on a loan approval workflow that exercises all three agent types and hook-driven regulatory override.
  arXiv  Detail & Related papers  (2026-06-25T15:50:42Z)
• Towards Verifiably Safe Tool Use for LLM Agents [53.55621104327779]
  Large language model (LLM)-based AI agents extend capabilities by enabling access to tools such as data sources, APIs, search engines, code sandboxes, and even other agents.<n>LLMs may invoke unintended tool interactions and introduce risks, such as leaking sensitive data or overwriting critical records.<n>Current approaches to mitigate these risks, such as model-based safeguards, enhance agents' reliability but cannot guarantee system safety.
  arXiv  Detail & Related papers  (2026-01-12T21:31:38Z)
• Blueprint First, Model Second: A Framework for Deterministic LLM Workflow [3.9886771197662925]
  We introduce the Source Code Agent framework, a new paradigm built on the "Blueprint First, Model Second" philosophy.<n>Our framework decouples the workflow logic from the generative model.<n>Our work enables the verifiable and reliable deployment of autonomous agents in applications governed by strict procedural logic.
  arXiv  Detail & Related papers  (2025-08-01T03:10:00Z)
• AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios [51.46347732659174]
  Large Language Models (LLMs) have demonstrated advanced capabilities in real-world agentic applications.<n>AgentIF is the first benchmark for systematically evaluating LLM instruction following ability in agentic scenarios.
  arXiv  Detail & Related papers  (2025-05-22T17:31:10Z)
• Agentic Knowledgeable Self-awareness [79.25908923383776]
  KnowSelf is a data-centric approach that applies agents with knowledgeable self-awareness like humans.<n>Our experiments demonstrate that KnowSelf can outperform various strong baselines on different tasks and models with minimal use of external knowledge.
  arXiv  Detail & Related papers  (2025-04-04T16:03:38Z)
• Formally Specifying the High-Level Behavior of LLM-Based Agents [24.645319505305316]
  LLMs have emerged as promising tools for solving challenging problems without the need for task-specific finetuned models. Currently, the design and implementation of such agents is ad hoc, as the wide variety of tasks that LLM-based agents may be applied to naturally means there can be no one-size-fits-all approach to agent design. We propose a minimalistic generation framework that simplifies the process of building agents.
  arXiv  Detail & Related papers  (2023-10-12T17:24:15Z)
• Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language [81.73820295186727]
  We propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following.
  arXiv  Detail & Related papers  (2021-09-16T20:36:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.