Context as the New Interface for AI-Driven Product Design
Designing seamless user experiences that transcend individual application boundaries through intelligent AI agents.
1The Fragmentation of Tasks
Modern digital experiences are often fragmented because products are designed around isolated tasks. Users must manually move context between calendars, messages, and booking apps to complete simple goals. This creates cognitive friction, as individuals act as the bridge between disconnected systems. AI agents now offer a way to unify these disparate interactions.
2Shifting from Apps to Context
The application-centric model is evolving into a context-driven one. Instead of users navigating specific interfaces, AI assistants interpret intentions and determine which capabilities are relevant. Apple’s App Intents framework exemplifies this, allowing developers to expose structured actions and entities to system-level experiences, enabling tasks to start outside the original application.
3Context as Design Material
Context is now a fundamental design material that must be actively structured and interpreted. It encompasses user preferences, previous decisions, and current constraints like calendar availability. Designers must decide which information is relevant at each stage, how it is shared between products, and what should remain private to ensure a coherent flow.
4The Role of Memory
Memory is critical when tasks span multiple products or timeframes. Systems must surface the context used for decisions, allowing users to verify or correct assumptions made by the AI. This prevents past behaviors from becoming unquestioned defaults, ensuring that continuity does not compromise the user's ability to maintain control over their choices.
5Structuring for AI Reliability
Reliable AI performance depends on how developers define entities, parameters, and actions. In frameworks like Apple’s App Intents, these definitions allow systems to identify correct items and perform actions accurately. These structural decisions are as vital as visual interface design, directly impacting whether a user understands the system's logic and intended outcomes.
6Implementing UX Governance
UX governance establishes the shared principles for how information is used across products. It defines access rights, delegation of actions, and approval requirements. Without these rules, cross-product experiences become inconsistent. Teams should utilize frameworks like the NIST AI Risk Management to identify risks, establish accountability, and manage system behavior over time.
7Standardizing System Connectivity
The Model Context Protocol provides a standard way for AI applications to connect with external tools and data sources. This standardization makes permissions and responsibility central to the design process. Designers must ensure that tool usage remains visible, providing enough information for users to follow the process and remain confident in results.
8Designing for Inherent Uncertainty
AI systems often misunderstand context, leading to incorrect inferences. Designers must account for this uncertainty by distinguishing between explicit user input and inferred data. When consequences are high, systems should require explicit confirmation. Effective design treats uncertainty as a core feature rather than an edge case, allowing for easy user corrections.
9Visibility in Multi-Step Tasks
When tasks involve multiple steps across different products, users need clear visibility into progress. They should see what has been completed, what is in progress, and what decisions remain. This transparency prevents the process from feeling unpredictable, helping users understand the system's plan and intervene if the AI makes a mistake.
10Redefining Product Value Metrics
Product teams no longer own the entire user journey. A product can provide significant value by contributing to a task that begins elsewhere, even if the user never opens its home screen. Consequently, traditional metrics like app sessions and screen time are becoming incomplete measures of a product's true utility and success.
11Collaborative Design Requirements
Governance and design for agentic systems cannot sit with one team alone. It requires deep collaboration between design, product, engineering, security, and legal departments. Using resources like the NIST AI RMF Playbook helps translate these complex responsibilities into practical activities, ensuring that the entire development process remains aligned with user safety needs.
12Future Design Priorities
Designers must prioritize mapping the context required for tasks, including information sources and product dependencies. By building on guidelines like Microsoft’s Human-AI Interaction principles, teams can identify recurring patterns for agentic workflows. The focus must remain on creating coherent experiences that guide users through uncertainty while maintaining clear, visible, and manageable system boundaries.
Original article: UX Collective - Medium — Context as the New Interface for AI-Driven Product Design