Type: Web Article Original link: https://arxiv.org/abs/2507.14447 Publication date: 2025-09-04
Summary #
WHAT - Routine is a structural planning framework for Large Language Model (LLM) based agent systems in enterprise environments. It provides a clear structure, explicit instructions, and parameter passing to perform tool calling tasks stably.
WHY - Routine addresses the issue of lack of domain-specific knowledge in common models, improving the stability and accuracy of tool calls in enterprise agent systems.
WHO - The main authors are researchers from academic institutions and tech companies, including Guancheng Zeng, Xueyi Chen, and others.
WHERE - Routine positions itself in the market of AI solutions for business process automation, enhancing the integration and effectiveness of agent systems.
WHEN - Routine is a relatively new framework, presented in July 2024, but already shows promising results in real enterprise scenarios.
BUSINESS IMPACT:
- Opportunities: Routine can accelerate the adoption of agent systems in enterprises, improving operational efficiency and the accuracy of automated operations.
- Risks: Competition with other planning frameworks may increase, requiring continuous improvement and differentiation.
- Integration: Routine can be integrated with the existing enterprise AI stack, improving the stability and accuracy of tool calls.
TECHNICAL SUMMARY:
- Core technology stack: Uses LLM models and structured planning frameworks. It does not specify programming languages, but it is likely to use Python and Go.
- Scalability: Routine is designed to be scalable, supporting multi-step tasks and efficient parameter passing.
- Technical differentiators: The clear structure and explicit instructions improve the stability and accuracy of tool calls, making Routine a robust framework for enterprise environments.
Use Cases #
- Private AI Stack: Integration into proprietary pipelines
- Client Solutions: Implementation for client projects
- Development Acceleration: Reduction in project time-to-market
Resources #
Original Links #
- [2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise - Original link
Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-09-04 19:35 Original source: https://arxiv.org/abs/2507.14447
The HTX Take #
This topic is at the heart of what we build at HTX. The technology discussed here — whether it’s about AI agents, language models, or document processing — represents exactly the kind of capability that European businesses need, but deployed on their own terms.
The challenge isn’t whether this technology works. It does. The challenge is deploying it without sending your company data to US servers, without violating GDPR, and without creating vendor dependencies you can’t escape.
That’s why we built ORCA — a private enterprise chatbot that brings these capabilities to your infrastructure. Same power as ChatGPT, but your data never leaves your perimeter. No per-user pricing, no data leakage, no compliance headaches.
Want to see how ready your company is for AI? Take our free AI Readiness Assessment — 5 minutes, personalized report, actionable roadmap.
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FAQ
How can AI agents benefit my business?
AI agents can automate complex multi-step tasks like data analysis, document processing, and customer interactions. For European SMEs, deploying agents on private infrastructure with tools like ORCA ensures that sensitive business data never leaves your perimeter while still leveraging cutting-edge AI capabilities.
Are AI agents safe to use with company data?
It depends on the deployment. Cloud-based agents send your data to external servers, creating GDPR risks. Private AI agents running on your own infrastructure — like those built on HTX's PRISMA stack — keep all data within your control. This is the safest approach for businesses handling sensitive information.