Docebo / 2026
AI Agents & Assistant
Designed one coherent AI experience that helps learners and administrators find answers, complete work, and govern specialized agents without learning a new interface for every capability.
Design Highlights
Overview
I work directly with senior leadership to translate Docebo’s AI-first direction into product experiences, with UX and quality central to the transformation.
I led the product experience for Docebo's AI Assistant and Agent Hub: a connected system that brings general assistance, custom agents, and specialized System Agents into the learning platform. The work spans the persistent AI entry point, panel and fullscreen conversations, chat history, Agent Hub discovery, and the controls administrators use to configure identity, access, knowledge, and actions.
Challenge
Docebo's growing AI capabilities needed to feel like one coherent product rather than a collection of disconnected assistants. Learners and administrators had to understand which agent was active, what context it could use, and what it was allowed to do. At the same time, administrators needed a manageable way to govern custom and System Agents without exposing the complexity of the underlying runtime, integrations, and permission model.
Approach
I designed the experience as a shared AI layer with clear identity, consistent conversation patterns, and context-aware specialization. I reconciled the live runtime and Figma system across the complete state surface: entry points, suggestions, composer behavior, history, panel and fullscreen modes, activation gates, attachments, deep links, and agent-specific controls. In Agent Hub, I organized configuration around the questions administrators actually need to answer: who can use this agent, what knowledge can it search, and which actions can it take?
Solution
AI Assistant is available from the core LMS shell and preserves the conversation as people move between a compact side panel and focused fullscreen view. Specialized System Agents adapt the same foundation to platform guidance, authoring, content discovery, reinforcement, in-course tutoring, and analytics. Agent Hub gives administrators one place to create, find, enable, and configure agents with explicit instructions, triggers, audiences, role requirements, approved knowledge collections, and external actions. Grounded answers expose sources and preserve existing access controls so assistance stays useful without becoming opaque.
Impact
Learners and administrators get one familiar place to ask for help and move between focused tasks without losing context. Customer organizations can introduce specialized agents while keeping identity, knowledge access, audiences, and actions understandable and governable. For Docebo, the shared experience model turns separate AI capabilities into an extensible platform foundation rather than a growing collection of disconnected interfaces.
Key Learnings
Agent platforms need more than a capable model. People need a clear sense of identity, scope, context, and control at every moment. The strongest design decisions made the system legible: persistent agent identity, permission-safe knowledge, visible sources, explicit audiences and roles, and deliberate boundaries around actions. Reusing one conversation system also made specialization feel coherent instead of fragmented.