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

SF Technology emphasizes that the real battlefield for logistics large models is not flashy technology but achieving an industrial‑grade closed loop that can "make decisions confidently and be accountable" across a trillion‑level business network.

To combat common industry issues such as model hallucination causing cargo imbalance or distorted scheduling commands, SF builds an anti‑hallucination system with a "rule compilation layer + logistics knowledge base + dynamic circuit breaker," which has improved airline scheduling punctuality by 7.2% over manual performance. For data security, a 100% private‑domain deployment intercepted 120,000 abnormal accesses, reducing leakage risk by 90% compared to public‑cloud solutions.

Responsibility means the AI agent must deliver results that are correct, timely, complete, and explainable; safety requires that core business knowledge be used while guaranteeing information security.

Responsibility is achieved by clearly defining the service scope of the agent and enhancing performance (reducing hallucination, improving inference, optimizing output).

Safety is ensured through strict monitoring, control mechanisms, and private server deployments that avoid external network interactions.

SF outlines the evolution of its AI agents from basic knowledge‑question answering to high‑level collaborative decision‑making. Early stages rely on native large‑model capabilities, prompt optimization, and knowledge‑base construction. Mid‑to‑high stages incorporate large language models, tool invocation, memory modules, and planning logic (e.g., ReAct framework) to perform multi‑step tasks and coordinate domain‑specific sub‑models.

The development roadmap includes:

Model fine‑tuning for specific logistics tasks.

Orchestrating large and small models, leveraging existing logistics decision models for flexible applications.

Building a logistics knowledge base to answer professional queries.

Prompt engineering tailored to logistics decision problems.

Integrating business workflows to teach models decision processes.

SF also provides low‑code AI agent development platforms such as Fengyu Assistant, and supports frameworks like Dify and LangGraph for more flexible agent services.

Key technical bottlenecks for scaling AI agents in logistics are:

Hallucination: high‑accuracy decisions cannot tolerate fabricated answers.

Inference performance: handling massive real‑time data with low latency.

Tool‑calling capability: interacting with operational systems, data platforms, and hardware.

The recommendation is to focus on domain‑specific problems rather than attempting to build all‑purpose agents.