AI Agent

🎯 The Challenge

Content teams spend significant time on repetitive operations like bulk updates, content restructuring, and taxonomy management. These tasks require enterprise-grade control and transparency but could benefit from AI automation.

🎨 Core Conversational Experience

Designed complete chat interface with sophisticated interaction patterns.

• Complete chat interface: welcome states, input patterns, message rendering
• Thinking indicators showing AI reasoning progress through multi-step tasks
• Progress visualization with real-time status updates for each step
• Interactive response components with copy, feedback, and action buttons

👁️ Transparency & User Control

Enterprise users need visibility into AI decisions and the ability to maintain control.

• Plan Approval Flow – users review and approve AI-generated plans before execution
• Reasoning Visibility – expandable sections revealing AI decision-making process
• Diff View Widget – side-by-side comparison of content changes
• Actionable Progress Links – clickable references to affected content items

💬 Conversation Management

Designed systems for managing complex, multi-turn conversations.

• Chat History Panel – searchable sidebar with conversation archive
• Smart Context Management – auto-generated conversation titles (editable)
• Context Switch Detection – proactive "This looks like a new topic" prompts
• Continuation Flow – intelligent detection of returning users

🧩 Key Design Challenges

Balancing conversational simplicity with enterprise requirements.

• Enterprise vs. Conversational Paradigms – Balancing chat simplicity with enterprise requirements for control and audit trails
• Transparent AI Without Overwhelm – Making AI reasoning visible without creating cognitive overload
• Maintaining Task Momentum – Designing approval flows that provide control without breaking user flow
• Multi-Step Task Design – Supporting both quick queries and complex multi-step workflows

🌊 Impact