Collaboration

Collaboration

Design for shared effort and mutual input

What it means

Autonomous agents should act as capable partners, not just tools waiting for commands. Collaboration means shared context, back-and-forth interaction, and joint ownership of outcomes. The agent contributes ideas, takes input, and improves the work in progress.

Why this matters

Collaboration builds stronger results than automation alone. When people and agents shape outcomes together, users stay engaged and push toward more creative, effective solutions.

Related patterns

Mixed initiative
This pattern fosters adaptive turn-taking between human and agent. It supports fluid collaboration by allowing both to lead based on context.
Mixed initiative
1

Agent proactively initiates based on context

Allow the agent to proactively detect issues or make suggestions, especially when it has useful context the user may not.

2

Build on Each Other’s Contributions

Design interactions so the agent can refine its outputs in response to human edits or questions. Keep the flow continuous and collaborative.

Co-editing interfaces
Shared content and workspace for both AI and humans foster transparency — building clarity and trust.
Co-editing interfaces
1

Keep AI suggestions non-intrusive

Present changes as proposals, not automatic edits. Let users review, accept, modify, or reject.

2

Work in shared view

Both AI and human should operate on the same content in the same workspace. Transparency builds clarity and trust.

3

User always has final say

The human is the editor-in-chief. AI assists, but never publishes or commits changes on its own.

4

Make editing modalities clear

Let users choose how the AI helps - proofreading, rewriting, suggesting changes, etc. Provide flexible control modes, not just one-size-fits-all.

Role clarity & turn signals
Clear handoffs and visible role signals reduce confusion in multi-step tasks. This principle makes collaboration feel more natural and efficient.
Role clarity & turn signals
1

Clear stage-based ownership

Break workflows into visible stages and indicate who leads each one (e.g., system, user, peer reviewer). This reduces ambiguity and improves accountability.

2

Attribute every action

Label actions clearly as system-initiated or human-initiated. This helps users interpret intent and trust the flow.

3

Signal when it’s the user’s turn

Use prompts, buttons, or callouts to indicate when the system is waiting for user input. Avoid passive steps that could confuse ownership.

How to implement

Define clear, intuitive ways for control to shift between human and AI
Let the AI learn from collaborative successes and adapt its behavior to match individual user styles and preferences
Maintain a unified workspace that tracks contributions, context, and progress from both human and AI participants
Gracefully handle simultaneous edits with merging, version comparison, or deferring to human review when needed

Common pitfalls

Lack of transparency
Users don’t understand how to influence the agent or override agent actions
Assumed alignment
The agent acts without confirming intent or context
Rigid flows
The system doesn’t adapt when users try to collaborate or redirect
Binary choices
Only offering accept/reject rather than co-create options