The Five Stages of Agentic Design
I was listening to Erik Satie's Gymnopédie No. 1 when it hit me. The way the melody emerges, almost imperceptibly at first, then gradually becomes something you can't imagine living without.
Satie understood something profound about presence and absence. In the 1920s, he invented what he called "furniture music"—compositions designed to blend into the environment rather than command attention. He envisioned music that would "fill the silence" without demanding focus, yet reward those who chose to listen deeply. Sound familiar? It's the same principle behind elevator music, lobby soundscapes, and every ambient experience we now take for granted.
But here's what most people miss: Satie's "invisible" music wasn't background noise—it was sophisticated composition disguised as simplicity. The harmonic ambiguity, the strategic repetition, the way silence becomes as important as sound. He was designing experiences that worked on multiple levels simultaneously.
We're building AI agents wrong because we're trying to skip straight to the finale without understanding the symphony and every User Experience solution, I realized, follows this same arc.
The Five Stages of Agentic Design Evolution
Stage 1: Invisible The AI works in the background, noticed only by its absence. This is Satie's "furniture music" principle applied to intelligence—sophisticated systems disguised as environmental simplicity. Like elevator music that subtly influences mood without demanding attention, or Satie's opening notes that are so subtle you might miss them, yet essential to everything that follows.
Examples: Spam filtering in your email. Fraud detection on your credit card. Auto-brightness on your phone. Route optimization in GPS. You never see these systems working, but you'd immediately notice if they stopped.
Stage 2: Substitution Now the AI can be used to substitute familiar tasks, but we're still thinking in old patterns. It's playing the same melody we've always known, just with different instruments. We automate existing workflows without reimagining them. Useful, but not transformative.
Examples: ChatGPT writing emails in your exact style. AI transcribing meetings you would have manually noted. Automated customer service responses replacing human scripts. Grammar checkers catching mistakes you'd normally proofread.
Stage 3: Reactive The AI starts to anticipate and respond. It offers suggestions and adapts to context. Like when Satie introduces those unexpected harmonies that make you lean forward. The system begins to show intelligence that feels genuinely helpful rather than just mechanical.
Examples: GitHub Copilot suggesting code based on your context. Smart reply suggestions that actually match your communication style. Calendar apps that notice conflicts and suggest alternatives. Photo apps that surface memories at emotionally relevant moments.
Stage 4: Amalgamation Here's where it gets interesting. The AI combines multiple tasks and contexts into something new. It's not just playing the notes—it's composing variations. Your AI can handle complex, multi-step processes while understanding the relationships between different parts of your work.
Examples: An AI assistant that notices you're traveling, automatically adjusts your calendar, books restaurants based on your dietary preferences and the weather, and shares your itinerary with relevant contacts. AI that combines your browsing patterns, calendar events, and email content to prepare briefing documents for upcoming meetings.
Stage 5: Intuitive The AI anticipates needs you didn't know you had. It operates on patterns so sophisticated they feel like intuition. This is Satie's final movement—where the music becomes inevitable, where you can't imagine the world without it.
Examples: An AI that notices subtle changes in your writing patterns and proactively schedules a wellness check. Systems that detect when you're entering a creative flow state and automatically minimize distractions. AI that understands the emotional subtext of your communications and suggests relationship-strengthening actions before conflicts arise.
Why This Matters
Most agentic design today is stuck between stages 2 and 3. We're substituting human actions without reimagining human possibilities. We're building sophisticated tools when we should be composing new symphonies of interaction.
But here's what's missing from most frameworks a focus on User Experience. While others ask "who adopts technology when?" or "what technical capabilities does our system have?", this framework asks the more fundamental question: "how does this solution work as it integrates into someone's life?"
Existing technology adoption models focus on demographics: innovators, early adopters, late majority. Agentic frameworks obsess over technical architecture like multi-agent coordination, tool integration, reasoning loops. These miss the phenomenological reality of how good solutions actually work.
This isn't just theoretical thinking for me. In my work on design and product at Aampe, where I focus on ML/AI and agentic infrastructure for personalization, I see these stages playing out in real time. True personalization follows this exact progression, from invisible algorithms that quietly improve your experience, through reactive suggestions, to eventually anticipating needs you didn't know you had. The most powerful personalization doesn't feel like personalization at all; it feels like the system just understands.
The real vision isn't about making AI that replaces us—it's about creating AI that reveals capabilities we didn't know we had. Each stage should feel as natural and inevitable as Satie's progressions, building toward something that transforms not just what we do, but how we think about what's possible.
The most profound solutions don't announce themselves. They become part of the fabric of experience, so seamlessly integrated that their absence would feel like missing notes in a familiar melody.
We're not just building agents. We're composing the future of human-AI collaboration, one subtle stage at a time.
