Designing for the Unknown:

Designing Human-Machine Interactions in an Autonomous Agent World

As AI shifts from automation to autonomous collaboration, designing effective human-AI interactions is crucial. At Outshift by Cisco, we focus on the Internet of Agents and the design principles needed to create experiences that will shape this new world.

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7 min

It's really clear, the recent introduction of OpenAI’s Operator tool signals a turning point in how AI integrates into real-world workflows—not as a passive assistant, but as an orchestrator capable of handling complex, multi-step tasks with autonomy. And in her first look at Operator, my colleague, Julia Valenti highlighted how OpenAI is shifting AI from a tool to a proactive collaborator, designing experiences where AI isn’t just responding to inputs but dynamically managing workflows.

At Outshift by Cisco, we see this as part of a much larger transformation—one that Vijoy Pandey has been championing: the rise of the Internet of Agents. This isn’t just about improving automation; it’s about designing an ecosystem where AI agents work together, make decisions, and evolve alongside human expertise.

The Evolution: From Simple Automation to Autonomous Collaboration

So yeah, It’s becoming clear—AI is no longer just a tool for automating tasks; it’s evolving into an active collaborator in workflows across industries. What once simply streamlined repetitive work is now transforming into AI systems that make decisions, anticipate needs, and orchestrate complex processes in real time.

This shift moves us beyond the era of transactional AI—where humans interacted with chatbots for quick, predefined answers—into a future where AI takes on more dynamic and strategic roles. As AI systems become more autonomous, the way we design for AI-human collaboration must evolve, bringing new challenges in trust, transparency, and usability.

This Internet of Agents evolution will take us through several stages:

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.

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:

Content

Probabilistic Operations

AI agents work with probabilities, leading to inconsistent outcomes and misinterpretations of intent.

Trust Over Time

Humans tend to trust AI teammates less than human teammates, making it crucial to build that trust over time.

Gaps in Contextual Understanding

AI agents often share raw data instead of contextual states, and may miss human nuances like team dynamics and intuition.

Challenges in Mental Models

Evolving AI systems can be difficult for humans to understand and keep up with, as the AI's logic may not align with human mental models.

The Solution:
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

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.

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

Make the Invisible Visible

Ensure Accountability

Ensure Accountability

Collaborate, Don't Just Automate

Earn Trust Through Consistency:

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.