Consumer vs. Enterprise
The design values and principles for AI agents vary depending on whether they’re built for consumers or enterprises. In this lesson, we’ll explore how those differences shape product decisions and user experience.
Note: Below is are the written notes of above video if you prefer reading:
As AI agents continue to evolve, it’s essential for product designers to understand how they show up in different environments - especially across consumer and enterprise contexts. While the underlying architecture (LLMs, memory, tools) may be similar, the design goals, user needs, and trust dynamics shift dramatically.
🧑💻 Consumer AI Agents
Consumer agents are designed for individuals and are often focused on emotional resonance, approachability, and everyday utility. They live in apps that people use for entertainment, productivity, communication, or self-reflection. These agents tend to emphasize tone, personality, and natural conversation.
Notable consumer agent examples:
Pi (Inflection) – Emotionally intelligent companion with memory and tone control.
Character.AI – Personality-rich agents used for creative storytelling and role-play.
Inworld AI – Agent frameworks used in gaming and entertainment; characters with emotion engines and goals.
Tolan – Voice-first personal agent designed for ambient, multimodal interaction.
ChatGPT (personal use) – Versatile assistant with tool/plugin support and custom GPTs.
Rabbit R1 (UFO) – Consumer hardware that uses agentic logic to operate apps visually via screen understanding.
Meta AI – Embedded in WhatsApp, Instagram, and Facebook; subtle but contextual agent built into social use cases.
Design characteristics:
Conversational-first and emotionally aware
Often playful, expressive, or branded
Focus on user agency, privacy, and personalization
Visibility and transparency matter — users want to know what the agent is doing and why
Delight, serendipity, and responsiveness can be just as important as pure accuracy
Designing for these agents often means building trust through tone, crafting onboarding that reduces cognitive load, and creating safe, bounded explorations.
For hands-on experience to understand how LLM's can be steered, try creating your own custom AI chatbot using ONE of the following products:
GPT's by OpenAI (Free)
Character AI (Free)
AI Studio by Instagram (Free)
Gems by Google (Paid)
ElevenLabs Conversational Call (Paid tokens) [Check out this example conversation agent of me as a teacher!]
🏢 Enterprise AI Agents
Enterprise agents, by contrast, are built for organizations and teams. They are focused on efficiency, accuracy, automation, and compliance. These agents often live in workplace tools, CRMs, or internal platforms and are judged not on charm — but on performance, auditability, and system reliability.
Notable enterprise agent examples:
Microsoft Copilot (365) – Embedded across Outlook, Word, Excel, etc., orchestrating productivity actions from user input.
Salesforce Einstein Copilot / Agentforce – Automates CRM tasks like follow-ups, forecasts, and data entry.
SAP Joule AI – A multi-agent system coordinating across business domains (HR, Finance, Ops).
Glean Chat – Enterprise knowledge agent that surfaces company-specific answers across docs, tickets, Slack, etc.
Claude (Anthropic) – Known for long-context reasoning and ethical alignment; popular in both enterprise and research contexts.
Gemini (Workspace) – Multimodal agent across Gmail, Docs, and Meet; acts on structured workplace data.
Cognosys – Autonomous agent that decomposes and executes work tasks across tools.
Intercom Fin Agent – Embedded support agent handling queries and workflow resolution.
Design characteristics:
Built to integrate deeply into workflows and internal tools
Emphasize reliability, access control, and permissions
Often operate in the background or through structured UIs (not always chat-based)
Users expect traceability and the ability to audit what the agent did
Guardrails, fallback logic, and safety are critical to maintain trust in enterprise settings
Designing for enterprise agents often means thinking about roles, data scopes, failures, and handoffs — not just what the agent can say, but what it’s allowed to do.
⚖️ What About Hybrid Agents?
Some agents sit in the middle - designed for individual users, but useful in professional or productivity contexts. These hybrid agents serve both personal and work goals, often with minimal switching friction.
Examples of hybrid agents:
ChatGPT (Pro / GPTs) – Used for everything from planning a trip to generating sales strategy decks.
Gemini (Google) – Embedded in both personal Gmail and Workspace accounts; tone and tool access vary by context.
Meta AI – Consumer-first today, but increasingly used in group chats or shared collaboration spaces.
Claude – Serves researchers, writers, and developers in both casual and enterprise contexts.
Designing hybrid agents means anticipating multiple modes of interaction:
Casual vs. professional tone
Tool access vs. privacy boundaries
Fast suggestions vs. rigorous completions
You may need to adapt the agent’s tone, transparency, or autonomy level depending on how and where it’s used. A one-size-fits-all approach can break down fast in hybrid contexts.
As an AI product designer, understanding this spectrum helps you ask better questions:
What does trust look like in this context: emotional or operational?
What degree of control should the user have : real-time or configurable?
What are the failure modes: and who pays the cost when something goes wrong?
Whether you’re designing a friendly companion or an invisible enterprise operator, remember: your job is not just to make the agent “smart”, it’s to make it feel helpful, trustworthy, and safe in the context it lives in.