Tezign Launches Agentic Transformation Co-Building Plan

Tezign officially launches the Agentic Transformation co-building plan, opening collaboration to industries, research, education, and creative fields. This plan addresses one of the most challenging aspects of AI implementation: enterprises have integrated models into processes but still cannot answer whether agents truly understand the business, whether judgments are reliable, and whether results can be measured. Tezign founder Fan Ling believes that enterprise AI applications are transitioning from single-point tools to deep transformation, and the efficiency gains from individual AI use do not equate to a simultaneous release of overall enterprise productivity.

Two World Models: Understanding People and Organizations

Enterprise-level agents need to possess two world models simultaneously. Tezign uses the Subjective World Model (SWM) to simulate human cognition, personality, and behavior; the enterprise world model answers what processes a decision will go through once it enters the enterprise and what resources and rules constrain it. Both are indispensable: with only the subjective world model, needs are understood but cannot be executed; with only the enterprise world model, tasks can be done efficiently but incorrectly. Agents do not call upon data, but context—the Context System carries these contexts.

Enterprise-Level Agent GEA and Long-Range Operating Mechanism

Tezign GEA (Enterprise-Level Agent System) is Tezign's self-developed enterprise-level agent system, with the core proposition of "enabling AI not only to answer questions but also to continuously push work towards results." It is supported by a three-layer architecture: the enterprise context (Context System) connects content, data, rules, relationships, and decision trajectories, forming a sustainable evolving enterprise memory; long-range agents continuously perceive signals, manage goals, plans, and states, advancing results amidst changes, and proactively return to humans when judgment and authorization are needed; production-level governance embeds evaluation, permissions, auditing, human supervision, and flexible deployment into every action. The long-range agent runtime mechanism allows agents to maintain goals and states across dialogues, systems, and time, continuously advancing business in a closed loop of "signal → planning → execution → optimization → back to new signals."

Fan Ling refers to this operational mode as business becoming agents. Traditional organizations are structured as people plus tools; AI-native organizations are structured as people plus agents, where agents push work to 80 points, and humans then elevate it from 80 points to 120 points. He also proposes moving from rented intelligence to sovereign intelligence: models are replaceable, data is controllable, and judgments are explainable, allowing intelligence to continuously add value with each real action.

Future Directions of the Co-Building Plan

The Agentic Transformation co-building plan will unfold in three directions: understanding, judgment, action, and evolution, advancing through collaborative research on real problems, project experiments, and method accumulation. Details of the co-building plan and subsequent progress can be obtained through Tezign Technology's official WeChat account and website; continuous attention is welcome.

The hallmark of agents entering enterprises is that decisions begin to leave a basis. Traceable and reusable judgment capabilities are the prerequisites for enterprises to entrust their business.