How SF Tech Secures AI Agents for Logistics: Responsibility, Safety, and Performance

When Baishi Group used DeepSeek to improve cross‑border document audit rates by 30% and Shaanxi Logistics leveraged the "QinWutong" large model for policy interpretation, SF Technology wrapped AI agents with four constraints—responsibility, safety, professionalism, and collaboration—to ensure industrial‑grade closed‑loop decision making.

To combat hallucination that can cause cargo mis‑allocation and distorted scheduling, SF built an anti‑hallucination system composed of a rule‑compilation layer, a logistics knowledge base, and dynamic circuit breaking, boosting on‑time air scheduling by 7.2% over human performance. For data security, SF deployed 100% private‑domain servers, intercepting 120,000 abnormal accesses and cutting leakage risk by 90% compared with public‑cloud solutions.

DataFun: How do you achieve responsibility and safety in vertical AI agents?

Liu Ziheng: Responsibility means the agent must deliver accurate, timely, complete, and explainable results; safety means using core business knowledge while guaranteeing information security.

Responsibility is ensured by clearly defining the service scope and improving performance—reducing hallucination, enhancing inference, and optimizing outputs.

Safety is achieved through strict monitoring, control mechanisms, and private‑deployment servers that avoid external network interaction.

DataFun: What are the key milestones for evolving AI agents from basic Q&A to high‑level collaborative decision making?

Liu Ziheng: Early stages focus on native large‑model capabilities, prompt optimization, and knowledge‑base construction for logistics Q&A. Mid‑to‑high stages require building agents with LLMs, tool invocation, memory modules, and planning logic (e.g., ReAct), enabling multi‑step tasks and coordination with domain‑specific sub‑models.

DataFun: How do you turn logistics expert experience into computable AI components?

Liu Ziheng: We employ several methods:

Model fine‑tuning for specific logistics tasks.

Orchestrating small domain models (e.g., routing optimization) with large models.

Constructing a logistics knowledge base to answer professional queries.

Prompt engineering tailored to logistics decision problems.

Integrating business workflows so the model learns decision processes.

DataFun: What technical bottlenecks must be overcome for large‑scale AI agent deployment in logistics?

Hallucination: Logistics decisions require high accuracy, so hallucination must be minimized.

Inference performance: Handling massive, real‑time data streams demands efficient context processing and low latency.

Tool‑calling capability: Agents must interact with operational systems, data platforms, and hardware reliably.

SF emphasizes building agents for specific problems rather than attempting all‑in‑one solutions.

DataFun: Does SF provide low‑code tools for business users to create AI agents?

Liu Ziheng: Yes, SF offers low‑code platforms such as Fengyu Assistant, and supports frameworks like Dify and LangGraph for more flexible development. Internal training, reference cases, and AI‑agent hackathons help business staff quickly prototype and deploy agents.

DataFun: What core capabilities must remain self‑controlled?

Liu Ziheng: Core logistics network decision‑optimization models, which are SF’s competitive edge, must be developed in‑house and remain fully autonomous.