Deva: A Platform for Modular AI Agents

Deva is a platform for creating, training, and interacting with modular AI agents. It’s the front-facing interface built on top of 10Planet — a backend system for decentralized memory and agent logic. Together, they enable long-term knowledge storage, interaction histories, and social workflows around personal or collective intelligence. Deva makes that invisible complexity feel intuitive and useful.

2022 — 2025

Principal Designer

Brand & UX Strategist

Product Design

Design System

3D & Motion Assets

Brand Identity

Brand Strategy

Marketing Design

Our goal with Deva was to build a system that lets users treat AI agents like collaborators — not tools. That meant designing for interaction, growth, and memory. The product had to feel approachable and flexible enough to support creators training agents, teams collaborating on shared agents, and networks evolving systems together. Above all, it had to make abstract ideas like “agent memory” or “training tokens” feel tangible and usable.

When we started, there was nothing to improve — because nothing existed. We were one of the first teams exploring the intersection of decentralized knowledge, AI agents, and memory systems. That meant building from zero: no reference UX patterns, no established agent management tools, and no shared visual language. Throughout the process, we pivoted multiple times — redefining flows, rethinking architecture, and even evolving the brand identity as the product vision matured.

We didn’t start with screens — we started with questions: What is an AI agent (Deva)? How does it store memory? What does long-term utility look like?

I mapped initial user journeys around creating, training, and deploying agents, then expanded flows to include memory management, agent discovery, and social interactions. Each iteration revealed blind spots, and the product evolved across multiple pivots — including a rebrand and a platform restructure.

We split the system into two connected products:

10Planet as the backend intelligence and agent management layer.

Deva as the front-facing experience for users to interact, train, and collaborate with their agents.

I knew we were entering a space with no established UX standards, so the only real direction was to focus on how people would feel interacting with something completely new. The goal wasn’t to impress with complexity — it was to make users feel like they could actually understand and use what they were seeing. Most of the product decisions were rooted in simplicity, clarity, and keeping the user grounded.

I spent time crafting flows that didn’t just work but made sense — especially when it came to helping users navigate abstract concepts like modular AI agents, voting mechanisms, or memory systems. There was a lot of thinking around when and how to introduce complexity, and how to use small patterns like tooltips or subtle animations to support onboarding without overwhelming. It was about guiding without forcing, opening up deeper parts of the product gradually and only when it was truly needed.

At the core of it, this was about making something experimental feel intuitive — building trust through the interface itself.

Deva’s identity evolved alongside the product. What started as a quiet, geometric design system grew into something more expressive — incorporating liquid-inspired assets, soft 3D objects, ambient motion, and custom UI microelements. The visual direction became a bridge between minimal tech and playful intelligence — subtle but alive.

I designed a first-of-its-kind system that feels both experimental and usable. Deva became functional, scalable, and expressive enough to support individual creators, teams, and future integrations. The work laid the foundation for other AI-native experiences — and set a high bar for designing around invisible systems like memory, trust, and logic.