If you're comparing agentic AI vs generative AI, you're asking a practical question: should AI create things for me, or do things for me?
Generative AI produces content—text, images, code—based on prompts. You ask, it generates. ChatGPT writing an email, DALL-E creating an image, Copilot suggesting code. The AI responds to what you ask, then waits for the next prompt.
Agentic AI takes action. It can plan multi-step workflows, use tools, interact with external systems, and pursue goals with minimal supervision. Instead of answering questions, it completes tasks.
The distinction matters because most real work isn't a single prompt-response cycle. It's a sequence of decisions, tool use, and iteration.
How generative AI works
Generative AI models learn patterns from training data and produce statistically likely outputs. Ask for a marketing email, get a marketing email. Ask for Python code, get Python code.
Strengths:
Fast content creation (drafts, summaries, brainstorming)
Wide knowledge from training data
Good at following specific formatting instructions
Limitations:
One turn at a time—no persistent memory across sessions
Can't take actions (send emails, update databases, browse the web)
No awareness of real-time context
Generative AI is a productivity multiplier for creating things. But it can't execute workflows, monitor conditions, or act on your behalf.
How agentic AI works
Agentic AI systems combine language models with:
Tool use—calling APIs, reading files, browsing the web
Planning—breaking goals into sub-tasks
Memory—maintaining state across interactions
Reasoning loops—evaluating results and adjusting approach
When you tell an agentic system "monitor my inbox for urgent emails and summarize them daily," it doesn't just generate a summary once. It runs on a schedule, accesses your email, applies judgment about what's urgent, and delivers results without you asking again.
The shift from generative to agentic is the shift from "AI as a tool" to "AI as a coworker."
When to use each approach
Use generative AI when you need:
A first draft (email, document, code)
Creative brainstorming
One-off transformations (summarize this, translate that)
Interactive Q&A
Use agentic AI when you need:
Multi-step workflows that run without supervision
Integration with external tools and data
Scheduled or triggered automation
Tasks that require real-time context (web data, files, APIs)
Many workflows combine both: generative AI creates content, agentic AI decides when and how to deliver it.
Building agentic systems on Zo
Zo Computer is built around the agentic paradigm. Your AI has:
A persistent server with your files and data
Tools to browse the web, read documents, and call APIs
Integrations with email, calendar, and external apps
Scheduled agents that run tasks autonomously
When you create an Agent on Zo, you're building an agentic AI system: a scheduled instruction that runs on your server, uses tools, and delivers results—without you being online.
Example agent instruction:
Every morning at 8am:
Check my Google Calendar for today's meetings
For each meeting, find the attendees' LinkedIn profiles
Summarize their roles and recent activity
Email me a briefing document
This is the difference between AI that answers questions and AI that does work.
The practical takeaway
Generative AI creates content. Agentic AI completes tasks.
If you're still copy-pasting outputs from ChatGPT into other tools, you're using generative AI as a stepping stone. The next step is letting AI handle the entire workflow—from gathering context to taking action to delivering results.
That's what agentic AI enables. And it's why "AI assistant" is evolving from "helpful chatbot" to "autonomous coworker."