Interaction Models
Six models for integrating AI into products, from embedded assistance to fully autonomous agents.
Interaction Models
Six distinct models for integrating AI into products. Each has different autonomy levels, user expectations, and design requirements. Choose based on task complexity, reversibility, and how much context-switching users can tolerate.
Comparison overview
| Model | When to use | Autonomy | Key risk | Real example |
|---|---|---|---|---|
| Embedded AI | User is mid-task, no context switch | Low — explicit invoke | Intrusive if unsolicited | Inline rewrite on selection |
| Copilot UX | Guidance alongside retained user control | Low–Med — suggests only | Panel ignored over time | Side-panel property insights |
| Agentic UX | Multi-step, complex, well-defined goals | High — executes goals | Loss of visibility + control | Research & report generator |
| Artifact-Centric | Rich documents, designs, or structured data | Med — direct manipulation | Unexpected scope changes | AI edits a listing document |
| Ambient / Proactive | Context + timing signals are reliable | Med–High — autonomous | Surveillance feeling | Price drop alert, smart sort |
| Multimodal | Modality flexibility is task-critical | Varies | Modality mismatch errors | Voice search → visual results |
Embedded AI — AI inside the interface surface
AI integrated into existing UI surfaces with no context switch. The user never leaves their primary task. Best suited for text transformation, smart fill, semantic search, and contextual actions on selected content.
Inline suggestions
Ghost-text completions appear as user types. Accept with Tab. Works for: search, code, email composition.
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Selection actions
AI options surface on text or element selection: Improve, Summarise, Translate, Expand. Zero friction.
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Smart autofill
AI completes form fields from context: previous inputs, user profile, document content. Show confidence.
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Contextual actions
AI options based on current view or item context, not text. 'Suggest similar listings' on a property page.
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Semantic search
Search interprets meaning and intent, not just keywords. Returns conceptual matches, not string matches only.
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AI-assisted navigation
AI suggests next steps or related content based on current context and usage patterns. Adaptive pathfinding.
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Copilot UX — persistent AI alongside the workflow
A persistent AI panel that lives alongside the primary interface — not a popup, not a separate page. Copilot maintains session-wide context awareness. Users retain full control; the AI offers, never imposes.
Side panel
AI assistance panel that opens alongside content, maintaining full context of the current view and recent actions.
Contextual copilot
AI adapts suggestions and capabilities to what the user is currently viewing or editing. A listing page gets different AI than a dashboard.
Command layer
AI accessible via ⌘K or command palette, accepting natural language commands that operate on the entire interface.
Workspace-aware AI
AI has memory of the session — open items, recent actions, user preferences — so suggestions are always relevant to the current work context.
Smart assistance
Proactive suggestions offered in the panel without interrupting the main flow. User can dismiss, accept, or explore without leaving their task.
Keyboard-first
Copilot features must be fully accessible via keyboard. Tab navigation, arrow key selection, Escape to dismiss. Mouse is optional.
Agentic UX — AI executing multi-step goals autonomously
The most complex AI model. An agent executes sequences of actions — searching, writing, calling APIs, modifying data — with significant autonomy. This demands the most careful design. Visibility, checkpoints, and interruption controls are non-negotiable.
Autonomy spectrum — start at level 1, earn higher levels through demonstrated trust
L1
Suggest
AI proposes, human does
L2
Confirm
AI does, step-by-step approval
L3
Delegate
AI does, checkpoint approval
L4
Automate
AI does, notification only
L5
Autonomous
AI does, no human involvement
Planning visibility
Show the agent's intended steps before execution. Users must be able to inspect and edit the plan before it runs.
Execution transparency
Real-time progress view: which step is running, what it found, what decision it made. Never a spinner with no context.
Approval checkpoints
At defined stakes thresholds, pause and surface a human decision point. Design these as meaningful choices, not dismissible alerts.
Interruption controls
Pause, stop, and undo must be always available and visually prominent. Agents that can't be stopped are a design failure.
Delegation patterns
Clear scope definition: what domains can the agent act in? What data can it access? Explicit permission grants, not broad defaults.
Recovery states
When an agent fails mid-task, show exactly where it stopped, what succeeded, and what needs human completion. Never silent failure.
Artifact-Centric UX — AI operating on user-owned objects
AI operates directly on documents, designs, spreadsheets, timelines, and codebases. The artifact is the interface — AI is an operator on it. This is fundamentally different from chat AI: the user never leaves the artifact.
Artifact types + relevant AI
Artifact AI principles
Changes visible in context
Always in the artifact — never in a separate result pane
Diff patterns mandatory
Before/after, track changes, per-item accept/reject
Scope always explicit
"You selected 3 paragraphs. I will only affect those."
Preserve direct manipulation
Keyboard shortcuts for accept/reject all must exist
Reversibility guaranteed
Every artifact AI action must be undoable instantly
Ambient & Proactive AI — acting without explicit prompting
The highest-risk model. AI acts on context signals without user trigger. Gets consent and transparency right or it feels like surveillance, not assistance. Applied carefully, it delivers the highest perceived intelligence.
Ambient AI types
Recommendations
Surfaces relevant content or actions based on inferred intent from behavioural signals.
Event-triggered
Activates in response to external events: new listing match, price change, deadline approaching.
Context-aware automation
Performs low-stakes actions when contextual signals are highly reliable and action is easily reversible.
Adaptive defaults
Interface and AI defaults shift based on learned usage patterns without explicit user instruction.
Trust & consent requirements
Show why AI acted
"I flagged this listing because you search this area weekly."
Action log
Persistent record of all ambient AI actions — timestamped and attributed.
Opt-out by default
Consequential actions are opt-in. Notifications are opt-out. Never assume consent.
Batch notifications
Notification fatigue destroys ambient AI trust faster than any other failure mode.
Multimodal AI — spanning input and output modes
AI that accepts and produces content across multiple modalities: text, image, voice, sketch, structured data, and generated UI. Mode-switching must feel natural, not technically imposed.
| Input | Output | Pattern | Adres example |
|---|---|---|---|
| Voice | Text + visual | Spoken search | "Find 2-bed in Marina" → listings grid |
| Image | Text | Visual analysis | Property photo → listing description draft |
| Text | Image | Text-to-visual | "Show me a sea-view layout" → generated floor plan |
| Sketch | Structured data | Intent extraction | Drawn floor plan → structured spec |
| Text | Generated UI | Dynamic interface | "Compare these 3 listings" → comparison table |
| Gesture + map | Action | Spatial AI | Map draw → AI filter by polygon |
Mode confirmation
Always show what modality AI interpreted the input as. Don't assume voice = search.
Fallback clarity
When AI can't process a modality, say so immediately with a concrete alternative.
Accuracy signalling
Confidence in modality parsing must be visible — especially for voice and sketch.
Smooth mode switching
Users switch modes mid-interaction. The interface must support this without reset.