Industrial Agent

Core capabilities of industrial Agents such as the safety verification layer, industrial tool library, and manual takeover system must be built in-house, and open-source frameworks should not be relied upon to solve industrial-grade reliability issues.

Industrial Agent scenarios have four core requirements: zero fault tolerance, strong controllability, low latency (millisecond-level response), and high reliability (stable operation 7×24 hours).

The control logic of industrial Agents must be pushed down to the edge; the cloud only handles data analysis, model training, and cross-site coordination, and on-site execution control cannot rely on the cloud.

An industrial Agent should be a complete closed loop of "perception—decision-making—execution—feedback—verification—correction"; an Agent lacking this closed loop cannot be considered truly deployed.

Large models cannot directly control devices; the correct approach is for the large language model to generate plans and decision recommendations, the rule system to review and verify them, and only after approval does the backend execute.

In industrial scenarios, three principles should be followed: verifiable content goes through RAG retrieval, uncertain content is never executed, and all decisions are fully logged.

The security mechanism of industrial Agents requires that high-risk instructions go through a whitelist, operations are tiered with authorization, and critical actions require secondary confirmation.

The real difficulty of industrial Agents lies in engineering rather than the model, with protocol integration, dirty data processing, anomaly detection, and device adaptation being the hard challenges in practice.

Stably integrating data from a single piece of legacy equipment into the system is often harder than fine-tuning a model.

Open-source Agent frameworks such as AutoGPT and LangGraph can only handle process orchestration responsibilities; core modules such as the safety validation layer, industrial tool library, and human takeover system must be developed in-house.

Industrial Agent safety mechanisms should include whitelisting for high-risk instructions, permission-based operation classification, and secondary confirmation for critical actions.

Industrial-grade Agents should not be implemented merely by connecting a large model with an added RAG layer, as this deployment approach cannot meet the admission threshold of industrial sites.

Industrial scenarios follow four iron rules: zero errors, must be controllable, millisecond-level response, and 7×24-hour stability.

The first principle of industrial Agent architecture design is to place real-time control on the edge side rather than in the cloud, with the cloud handling only analysis, training, and coordination tasks.

An industrial Agent is a complete closed loop of perception → decision → execution → feedback → verification → correction, and after an instruction is issued, the system must confirm whether execution was successful.

Industrial Agents should adopt a two-layer structure in which the large model is responsible for proposing decisions and the rule system is responsible for auditing and validation, with instructions only permitted to be issued after the rule layer has approved them.

Governing hallucinations requires three things: use RAG for content that can be verified, never execute content that is uncertain, and keep a full audit trail of all decisions.

The industrial Agent demonstrated by Haier COSMOPlat can break down production tasks into code generation, simulation verification, and production line deployment.