Designing for the Unknown:

Enhanced AI Conversation Navigation and Management for User Action Assistant

Knowledge workers have diverse levels of ability when it comes to engaging with AI Assistants and traditional AI chat experiences are linear and restrictive.

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Navigating a conversation with an AI Assistant…sucks. Chats are long strings of prompts and responses that result in a captive linear effect that often includes inaccurate or unhelpful AI responses making it hard for a user to cobble together an effective outcome from the chat. And the longer more complex the conversation is, the harder it is for the user to piece together what was said and how to create an effective outcome from all that conversation.

Instead AI chats should be flexible conversations where the prompt and responses are movable, editable nodes that can be plucked out and constructed into effective and clear narratives.

The interface would be similar to a digital white-board where users can drag and drop elements to create the most effective narrative from the lengthy, linear conversation.

So, that gives us a world where AI agents can discover and authenticate one another, share complex information securely, and adapt to uncertainty while collaborating across different domains. And users will be working with agents that will pursue complex goals with limited direct supervision, acting autonomously on behalf of them.

As a design team, we are actively shaping how we navigate this transformation. And one key question keeps emerging: How do we design AI experiences that empower human-machine teams, rather than just automate them?

The Agentic Teammate: Enhancing Knowledge Work

In this new world, AI agents become our teammates, offering powerful capabilities:

Knowledge Synthesis: Agents aggregate and analyze data from multiple sources, offering fresh perspectives on problems.

Scenario Simulation: Agents can create hypothetical scenarios and test them in a virtual environment, allowing knowledge workers to experiment and assess risks.

Constructive Feedback: Agents critically evaluate human-proposed solutions, identifying flaws and offering constructive feedback.

Collaboration Orchestration: Agents work with other agents to tackle complex problems, acting as orchestrators of a broader agentic ecosystem.

The Agentic Teammate: Enhancing Knowledge Work

In this new world, AI agents become our teammates, offering powerful capabilities:

Knowledge Synthesis: Agents aggregate and analyze data from multiple sources, offering fresh perspectives on problems.

Scenario Simulation: Agents can create hypothetical scenarios and test them in a virtual environment, allowing knowledge workers to experiment and assess risks.

Constructive Feedback: Agents critically evaluate human-proposed solutions, identifying flaws and offering constructive feedback.

Collaboration Orchestration: Agents work with other agents to tackle complex problems, acting as orchestrators of a broader agentic ecosystem.

Addressing the Challenges: Gaps in Human-Agent Collaboration

All this autonomous help is great, sure – but it's not without its challenges.

Autonomous agents have fundamental gaps that we need to address to ensure successful collaboration:

The Agentic Teammate: Enhancing Knowledge Work

In this new world, AI agents become our teammates, offering powerful capabilities:

Knowledge Synthesis: Agents aggregate and analyze data from multiple sources, offering fresh perspectives on problems.

Scenario Simulation: Agents can create hypothetical scenarios and test them in a virtual environment, allowing knowledge workers to experiment and assess risks.

Constructive Feedback: Agents critically evaluate human-proposed solutions, identifying flaws and offering constructive feedback.

Collaboration Orchestration: Agents work with other agents to tackle complex problems, acting as orchestrators of a broader agentic ecosystem.

Empowering Users with Control

Establishing clear boundaries for AI Agents to ensure they operate within a well-defined scope.

Designing Tomorrow's Human-Agent Collaboration At Outshift

These principles are the foundation for building effective partnerships between humans and AI at Outshift.

Building Confidence Through Clarity

Surface AI reasoning, displaying: Confidence Levels, realistic expectations, and the extent of changes to enable informed decision-making.

Always Try To Amplify Human Potential

Actively collaborate through simulations and come to an effective outcome together.

Let Users Stay In Control When It Matters

Easy access to detailed logs and performance metrics for every agent action, enabling the review of decisions, workflows, and ensure compliance. Include clear recovery steps for seamless continuity.

Take It One Interaction at a Time

See agent actions in context and observe agent performance in network improvement.

Addressing the Challenges: Gaps in Human-Agent Collaboration

All this autonomous help is great, sure – but it's not without its challenges.

Autonomous agents have fundamental gaps that we need to address to ensure successful collaboration:

Addressing the Challenges: Gaps in Human-Agent Collaboration

All this autonomous help is great, sure – but it's not without its challenges.

Autonomous agents have fundamental gaps that we need to address to ensure successful collaboration:

The Solution:
Five Design Principles for Human-Agent Collaboration

What to Consider:
Five Design Principles for Human-Agent Collaboration

Put Humans in the Driver's Seat

Users should always have the final say, with clear boundaries and intuitive controls to adjust agent behavior. An example of this is Google Photos' Memories feature which allows users to customize their slideshows and turn the feature off completely.

Make the Invisible Visible

The AI's reasoning and decision-making processes should be transparent and easy to understand, with confidence levels or uncertainty displayed to set realistic expectations. North Face's AI shopping assistant exemplifies this by guiding users through a conversational process and providing clear recommendations.

Ensure Accountability

Ensure Accountability

Anticipate edge cases to provide clear recovery steps, while empowering users to verify and adjust AI outcomes when needed. ServiceNow's Now Assist AI is designed to allow customer support staff to easily verify and adjust AI-generated insights and recommendations.

Collaborate, Don't Just Automate

Prioritize workflows that integrate human and AI capabilities, designing intuitive handoffs to ensure smooth collaboration. Aisera HR Agents demonstrate this by assisting with employee inquiries while escalating complex issues to human HR professionals.

Earn Trust Through Consistency:

Build trust gradually with reliable results in low-risk use cases, making reasoning and actions transparent. ServiceNow's Case Summarization tool is an example of using AI in a low-risk scenario to gradually build user trust in the system's capabilities.

Designing Tomorrow's Human-Agent Collaboration At Outshift

These principles are the foundation for building effective partnerships between humans and AI at Outshift.

As we refine our design principles and push the boundaries of innovation, integrating advanced AI capabilities comes with a critical responsibility. For AI to become a trusted collaborator—rather than just a tool—we must design with transparency, clear guardrails, and a focus on building trust. Ensuring AI agents operate with accountability and adaptability will be key to fostering effective human-agent collaboration. By designing with intention, we can shape a future where AI not only enhances workflows and decision-making but also empowers human potential in ways that are ethical, reliable, and transformative.

Because in the end, the success of AI won’t be measured by its autonomy alone—but by how well it works with us to create something greater than either humans or machines could achieve alone.