arXiv:2607.03228v1 Announce Type: new 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 ru
The proliferation of large language models (LLMs) and the increasing focus on their autonomous capabilities are driving advancements in agentic systems for complex business processes.
This development addresses a critical limitation of current AI applications in enterprises by enabling LLMs to integrate organization-specific knowledge for reliable, automated execution, moving beyond rigid rule-based systems.
The ability to imbue AI agents with dynamic, organizational memory transforms how enterprises can automate white-collar workflows, potentially collapsing traditional SaaS layers and reducing manual overhead.
- · AI platform providers
- · Enterprises adopting AI agents
- · Generative AI developers
- · Legacy business process management (BPM) vendors
- · Consulting firms focused on manual process optimization
- · Knowledge management as a standalone function
Enterprises begin to successfully deploy AI agents that autonomously execute complex, knowledge-intensive business processes.
The demand for human white-collar labor in administrative and operational roles significantly decreases, leading to workforce restructuring.
New business models emerge around highly personalized and dynamic agent-driven services, fundamentally altering competitive landscapes.
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Read at arXiv cs.AI