Why is enterprise AGI the real battlefield for AI? Because AI's deepest business value will not stop at an email or a slide deck on an office desk. The harder part happens in the offices of company owners, on factory floors, in stores, warehouses, supply-chain hubs, and frontline business sites.
That is why Palantir, after more than two decades, is being reinterpreted in the United States. It tells the story of exploring how to become an enterprise AI operating system: in traditional enterprises, data is scattered across departments and software, making it hard for AI to truly understand business reality. Through Ontology, Palantir connects data, processes, business objects, and decision chains, giving enterprises a real-time operating map and moving AI from answering questions to participating in decisions, helping managers analyze, judge, decide, and drive execution.
In China, PKU-rooted enterprise AGI company AHS is telling a similar but more aggressive story. Founded in 2021, AHS is not satisfied with becoming "another Palantir." It positions itself as an AI-native "evolved Palantir": first organizing enterprise data, processes, and decision chains into business knowledge graphs that AI can read, then allowing agents to assist judgment and participate in execution; every task execution then becomes reusable industry experience that feeds back into the platform, helping the next generated knowledge graph and digital workforce better understand the industry, process, and organization.
Supporting this narrative is AHS Agentrix, built by AHS on top of its world behavior model.
The refined design paradigm of enterprise AGI: decision rights remain human
Agentrix is an integrated agent platform with a three-layer architecture: the underlying Data OS, the middle Agent OS, and the top Agent Workforce. Its core path moves from a data and knowledge foundation to a central decision brain, and then to clusters of digital employees.
At the Data OS layer, business data such as store transactions, factory production, machine operations, staff scheduling, inventory changes, and work-order flows are no longer just records scattered across systems. They are fused, extracted, vectorized, and modeled through business knowledge ontologies, becoming unified "cognitive material" that agents can understand, call, and reason over.
AHS calls this process "super alignment": reconnecting people, goods, places, equipment, processes, and decision logic inside the same intelligent relationship network.
Truly valuable enterprise knowledge is often hidden in approval flows, work orders, reports, exception handling records, management experience, and frontline operating habits. Data OS does not simply let AI "know what something is"; it reasons layer by layer over the enterprise's own structured data to determine "what should be done under what conditions."
The concern that makes enterprises want to use AI yet hesitate is that models may give answers that appear reasonable but are wrong when they lack business grounding. This kind of "data hallucination" is greatly suppressed through Data OS data alignment and ontology modeling.
If Data OS builds the cognitive foundation for enterprise intelligence, Agent OS is responsible for constructing the enterprise intelligence hub.
In the past, enterprise AI was often a set of point applications. Digital tools could improve local efficiency, but rarely changed how the whole organization operated. Agent OS allows hundreds or even tens of thousands of agents to run within the same organizational logic. They supervise, collaborate with, and score one another, while accumulating experience through task execution.
Agentrix represents an advanced paradigm for enterprise AGI. Its refined design lies in emphasizing agent collaboration while upholding human decision rights.
The business closed loop produced by Agent OS is discovery -> analysis -> recommendation -> human confirmation -> execution -> supervision -> result feedback. The AHS team insists that humans must remain at the center of business-flow decisions. On the basis of organizational controllability, Agent OS acts as an enabling layer, allowing AI to more efficiently support decision assistance and task execution.
Human decision rights and traceable data are prerequisites for enterprises to "dare" to let AI enter core business processes.
In addition, every piece of data is traceable. "Every conclusion, recommendation, and action given by Agent OS can be traced back to the enterprise's own original data."
At the Agent Workforce layer, AI finally grows hands and feet. Agents are assigned roles, permissions, rules, and task boundaries, becoming digital employees that execute tasks. They may be financial analysis agents, supply-chain optimization experts, compliance officers, auditors, safety inspection specialists, vehicle fault-diagnosis specialists, and more.
FDE + FSE frontline squads deployed on the customer's business front line
However, the difficulty of enterprise AGI is not only building the product architecture. The truly hard part is deploying that architecture into the customer's business site.
AHS's approach is to send frontline deployment squads composed of FDEs (Forward Deployed Engineers) and FSEs (Forward Solution Engineers).
In the traditional SaaS era, software companies relied on standard products, remote implementation, and customer success teams for delivery. Enterprise AI is different: it faces legacy systems, non-standard processes, and permission boundaries inside the customer organization. The information that truly determines "productivity" is often hidden in frontline employees' operating habits, collaboration nodes between departments, and management experience and judgment.
AHS brings FDEs and FSEs into customer sites together, where they serve as a "modeling" interface between the customer's business reality and the agent platform, working with clients to map data, processes, roles, and decision chains.
FSEs define business problems, such as which workflows are worth handing over to agents, which nodes must retain human confirmation, and which tasks can be automatically closed. FDEs then turn these judgments into system integrations, data modeling, agent orchestration, and execution feedback mechanisms.
The value of this approach is that it creates a two-way cycle between the capabilities of the Agentrix agent platform and the customer's front line. On one side, the frontline deployment squad brings AI-native development processes into the customer's operating system, helping the customer mobilize the organization from process mapping to agent deployment. On the other side, the general industry rules and knowledge accumulated in each project flow back into Agentrix as structured assets that can be reused in the next delivery.
Therefore, Agentrix does not evolve by stacking features like traditional SaaS. It is a continuously self-reinforcing closed loop: industry knowledge and business rules enter the platform; the decision brain generates judgments from them; agents execute tasks after human decisions; and execution results flow back as new experience. Every project delivery and every completed task, in turn, improves the platform's understanding, abstraction, and reuse of business.
An Agentrix solution can be understood as "80% general cognitive core + 20% industry adaptation parameters." Once general industry knowledge is abstracted, modeled, and converted into high-dimensional vectors, it can release value in more specific vertical scenarios. This also makes AHS's positioning as an "evolved Palantir" more explanatory.
PKU DNA and the next-generation enterprise AGI platform for the AI era
Compared with Palantir, which grew out of the internet era, AHS believes it has the conditions of AI-native organization, self-evolving systems, edge-cloud collaboration, and China's engineering environment, allowing it to form a next-generation enterprise AGI platform faster in the AI era.
Edge-cloud collaboration is especially critical. Edge nodes such as industrial computers on factory floors, handheld terminals in warehouses, and POS systems in stores may all need low-latency, highly reliable agent capabilities. For tasks such as safety control, onsite dispatching, and real-time response, operations cannot depend entirely on the network.
AHS's vision is to deploy lightweight agents on edge devices to complete low-latency local interaction and local execution of critical tasks, then form a closed loop with cloud-based intelligent systems.
Customer cases make this logic visible. In urban governance, finance, manufacturing, healthcare, government affairs, and other scenarios, Agentrix already has practical deployments, with partners including leading listed Chinese companies such as Qiaoyin Group and China Merchants Shekou.
The ultimate battlefield of enterprise AGI lies in the capillaries of real business operations.
Agentrix is AHS's system-level answer to the enterprise AGI battlefield. The technical foundation behind this answer is a core technical team with Peking University roots. Public information shows that more than 80% of AHS's R&D staff come from Peking University.
AHS's head of core technology and CTO, Guo Lin, previously served as a master's supervisor at Peking University's School of Software and Microelectronics. He has long worked in computer graphics, computer vision, high-performance computing, AI data governance, and related fields, and is a key operator behind AHS's world behavior model and system engineering architecture. Guo Lin has also been a serial entrepreneur for 12 years, repeatedly building technology platforms from zero to one and serving large enterprise customers such as Microsoft and Siemens. He is also an important promoter of AHS's FDE + FSE frontline deployment squads.
Republished fromLeiphone
