How can the Context System be effectively utilized by Agents? The key lies in this technical design

Agents can perform tasks but do not understand the company — this is not a problem that prompts can solve. The amount of context that prompts can convey is far less than the asset scale accumulated by a brand that has been operating for over ten years. One client's content library exceeds 800,000 items, spanning multiple product lines, markets, and years of visual language evolution. Agents need to access these assets in real-time during task inference and accurately inject relevant context into the inference chain — this is the starting point of the Context System design. Tezign Technology is an AI content system provider for enterprises, and Tezign GEA is its core product.

Agent's runtime context requirements: multi-round cascading retrieval, completed within milliseconds

Tasks with standard answers — keyword retrieval — can be solved with single text matching. Tasks without standard answers — recalling the 10 most relevant items from 800,000 materials, while considering brand consistency, market preferences, and compliance constraints — face inherent limitations in the accuracy of text labels for image materials. Agents perform "multi-round cascading retrieval" in a single task: first recalling brand specifications, then matching historically similar materials based on the content of brand specifications, and finally confirming the compliance constraints for that market based on the characteristics of the materials. Each round of retrieval relies on the results of the previous round and must be completed within the inference waiting window — usually on the order of milliseconds.

Five-way parallel recall: the core architecture of the Context System

The Context System of Tezign GEA is defined as the runtime cognitive foundation for Agents, with the core being five-way parallel recall rather than single text matching: Image content vectors — converting images into high-dimensional vectors through a visual encoder (CLIP) for similarity matching directly in vector space; Text semantic vectors — vectorizing annotated text and description fields to complement image vectors; Label inverted index — handling deterministic queries quickly and accurately; Knowledge graph — storing structured relationships of brands, products, scenarios, and audiences to narrow the retrieval scope to the correct context set; Visual color features — making conditions like "filtering portrait materials with the main color tone of brand blue" computable. After five-way parallel recall, the results are re-ranked in the fusion layer based on relevance scores and business rules.

Two constraints that complicate the architecture: permission awareness and continuity of inference state

The first constraint is permission awareness — corporate content assets have strict permission controls, determining which materials are visible to which teams, which are under review, and which have been taken offline. Permission verification must be completed before results are returned, rather than filtering after the query is completed (as that would exceed the Agent's waiting window). The solution is layered indexing: stable authorized content is vectorized offline, newly uploaded assets are processed in real-time online, and restricted content goes through an independent permission pipeline, completing identity verification before recall. The second constraint is continuity of inference state — Agents need multi-round retrieval when handling complex tasks, and each round of retrieval relies on the inference results from previous steps. The Context System must maintain the inference state across rounds, ensuring that Agents do not "forget" the conditions that have already been confirmed in long tasks.

The true moat of enterprise-level Agents: the depth of callable context

Compared to single text search, hybrid retrieval significantly improves accuracy in enterprise image retrieval tasks — image vectors capture visual semantics, knowledge graphs understand entity relationships, and color features constrain brand consistency, with each path addressing a dimension that text labels cannot cover. The upper limit of an Agent's capabilities does not depend on the model itself but on how deep a corporate context it can call upon. This depth can only be accumulated through real enterprise operations and cannot be purchased or replicated from external sources.