Two years ago, the interface for the personal AI tools illustrated a simpler approach. When users asked about digital product design, the system searched the creator’s articles, videos, and other materials, pulled in relevant excerpts, and cited them directly in responses. A sidebar displayed the specific sources used.
If a user opened one of those sources—such as an article or PDF—the interface added a small “context chip” to the question bar, indicating that subsequent queries would apply only to that file. Removing the chip returned the scope to the full library of materials. This system made it clear which resources were in use and allowed quick toggling between them.
Expanding Context and Scaling Challenges
As AI products grew more complex, so did their context-management interfaces. Augment Code, for example, employs multiple context chips to represent retrieval systems, active files, and selected text simultaneously. However, when many elements are displayed, the interface can become crowded, forcing truncation of names and diminishing clarity.
Additional complexity arises in systems with automatic context retrieval. Designers must decide whether to surface every item retrieved—potentially overwhelming users—or allow the process to remain invisible and rely on user trust.
Agent-Oriented Workflows and Bench’s Approach
AI products that employ agents introduce another layer of complexity, as each tool or sub‑agent may independently gather or generate context. Early versions of Bench displayed context dynamically as agents worked, but this frequent updating created a disjointed experience.
The current Bench interface instead condenses the agent’s activity into a series of steps, each linked to the underlying context. Users can review any step for details on what was retrieved or generated without being interrupted by constant updates. When sub‑agents are involved, Bench aggregates their combined context into a single link, allowing users to inspect cumulative inputs without tracking each sub‑process individually.
Shifting User Priorities
Although these design evolutions provide greater transparency, many users focus primarily on outputs rather than the process behind them. Context links and process timelines tend to be consulted only when results appear incorrect. As user confidence in AI systems increases, designers may further reduce the visibility of context-management features, balancing transparency with a streamlined interface.
Conclusion
Managing and displaying context remains a core challenge in AI product design. From simple chips in early systems like Ask LukeW to step-based context links in tools such as Bench, approaches continue to evolve. The trend suggests that while transparency options are available, their prominence in user interfaces may decrease as users prioritize results over process visibility.