AI Copilot vs Agentic AI
Apr 15, 2025
Generative AI continues to reshape how we work. As I am having conversations about designing for AI at my work at Palo Alto Networks, we're rethinking how cybersecurity professionals go about their day-to-day tasks. Supplementing that with how I use AI, I'm observing two dominant AI usage patterns: using AI to augment our tasks and, using AI to replace redundant tasks. These usage patterns reflect two emerging design patterns in how AI is being incorporated into systems—the Copilot model and Agentic AI (the latest buzzword making its rounds). These patterns differ fundamentally in their design approach and interaction models. While we're all still learning what to build and how to build it, I believe the choice between these models significantly determines how humans will interact with AI-powered systems.
How are these models different?
"The short answer? It's assistance vs autonomy."
The Copilot model operates on collaboration. The AI acts as an intelligent assistant that augments whatever task you're doing. Think of it as your sous chef who preps ingredients, suggests techniques, and helps you execute your vision - but you're still the head chef making the creative decisions and leading the kitchen. You maintain control over decisions, workflows, and outcomes. The AI provides suggestions, surfaces important information, handles routine tasks, and amplifies your intelligence.
The Agentic model works with much higher autonomy. It's like having a meal delivery service that takes your dietary preferences and delivers complete meals - all you need to do is share your goals and preferences. Agentic systems take on complete workflows with minimal human intervention. They're designed to achieve goals independently, demonstrating planning and decision-making within their defined scope of authority.
When to Use Which Model?
"Does the task require human judgment or can it be systematized?"
Tasks requiring human judgment thrive with copilot models, while routine and repetitive tasks benefit from agentic approaches.
The Copilot model excels at enhancing existing workflows, particularly those involving strategic decision-making, complex problem-solving, and creative work. These areas continue to require human emotional intelligence and contextual understanding that AI can't fully replicate. Back to our kitchen example: a copilot is perfect for menu planning (suggesting seasonal ingredients based on availability), recipe development (recommending flavor pairings you might not have considered), or presentation ideas (showing plating techniques from similar dishes). You're still the creative force, but the AI helps you explore possibilities and refine your vision.
The Agentic model shines with repeatable tasks that follow routine patterns and have clear success criteria. Think of inventory management, supplier ordering, or food safety compliance checks—tasks that require systematic execution but don't need creative input. In our kitchen example, an agentic system could automatically reorder ingredients when stock runs low, schedule equipment maintenance based on usage patterns, or generate daily prep lists based on reservations and menu requirements. These systems scale operations beyond human capacity while maintaining consistency and accuracy.
Rather than viewing these models competitively, the AI products strategically combine both the approaches. This hybrid approach leverages the strengths of each while mitigating individual limitations. An effective strategy is starting with copilot assistance to build trust, then gradually delegating routine tasks to autonomous agents as patterns start to emerge.
What Should you Consider while Designing for Each of these Models?
The design approach for each model requires fundamentally different considerations.
For Copilot experiences, the challenge lies in being helpful without being intrusive—you wouldn't want the sous chef offering soup recipes while you're making dessert. The AI needs to be transparent without overwhelming users, adaptive without being unpredictable, and discoverable without interrupting flow. Success depends on making the AI feel like a natural extension of the user's workflow rather than a separate tool demanding attention.
For Agentic systems, the design script flips entirely. The primary challenge shifts from "how do we help?" to "how do we communicate what we're doing?" Users need clear visibility into autonomous operations, reliable ways to intervene when needed, and continued assurance that the system operates within defined boundaries. The design must balance automation's efficiency with the human need for control and understanding.
What's Next
The choice between copilot and agentic AI patterns might not be binary - it's about matching the right pattern to the right context and designing systems that can gracefully transition between different modes of interaction. As AI capabilities continue to advance, the most successful products will be those that thoughtfully combine these patterns to create experiences that feel both powerful and trustworthy.
The insights and perspectives shared in this article are based on my ongoing learning from various sources including industry publications, design conferences, conversations with peers, hands-on experience with AI tools, and observations from my design practice. As the field of AI UX continues to evolve rapidly, these viewpoints reflect my current understanding and may evolve as new patterns and best practices emerge.