Pattern Category
Human-AI Collaboration
Human-AI collaboration patterns define how control is shared between people and AI. These patterns cover when AI should act, when it should ask, and how it should hand off gracefully — the design decisions that make AI feel like a teammate instead of a tool.
11 patterns in this category
Contextual Assistance
Offer timely, proactive help and suggestions based on user context, history, and needs.
Human-in-the-Loop
Balance automation with human oversight for critical decisions, ensuring AI augments human judgment.
Augmented Creation
Empower users to create content with AI as a collaborative partner.
Collaborative AI
Enable effective collaboration between multiple users and AI within shared workflows.
Feedback Loops
Continuous learning mechanisms where user corrections and preferences improve AI performance, creating experiences that evolve with usage.
Graceful Handoff
Seamless transitions between AI automation and human control.
Autonomy Spectrum
Provide a spectrum of autonomy levels - from passive suggestions to full autonomy - that users can adjust per task type, enabling granular control over how independently an AI agent operates.
Intent Preview
Before any significant action, the agent presents a clear, scannable summary of what it intends to do - showing planned steps, reversibility status, and edit controls for user approval.
Escalation Pathways
Design structured escalation triggers and handoff mechanisms so agents can pause and ask for human guidance when they encounter ambiguity, conflicts, or decisions beyond their authorization - without breaking workflow or losing context.
Mixed-Initiative Control
Design interaction models where control flows seamlessly between human and agent - supporting parallel work zones, interruptible agent activity, and natural handoffs without formal 'take over' actions.
Workspace-Native Agent Integration
Embed AI capabilities inside existing tools so users never leave their working context to get help.