InEvent解析：会展策划中代理式AI与生成式AI的区别及应用策略

Posted on October 26, 2026

Most event teams already use generative AI. You ask, it writes. Agentic AI is the next step, and the difference between the two is not intelligence. It is permission. A generative tool hands you a draft. An agentic tool takes an action.

That changes the planner’s job. The skill is no longer writing a good prompt. It is deciding what a machine may do on your behalf, and that, in our view, is the real AI decision event teams face. Most “which AI tool is best” debates skip it.

This piece draws on the InEvent Open House of October 8, 2026, where Katie McIntyre, Director of Technical Architecture at Opus Agency, and Wendy Porter, Founder and Chief Event Strategist at Wendy Porter Events, explained how they use both kinds of AI in real event work.

Table of Contents

• What is the difference between agentic AI and generative AI?
• Agentic AI vs generative AI examples in event planning
• What can an AI agent do for an event team?
• Where should a human stay in the loop?
• The bottom line
• Frequently asked questions

What is the difference between agentic AI and generative AI?

Generative AI creates content when you prompt it, then waits for your next instruction. Agentic AI takes a goal, decides its own steps and carries them out, often inside other software, without checking in at each step. One gives you drafts to approve. The other gives you actions you have to trust.

Katie McIntyre put the difference simply at the Open House. Generative AI “is responding to your prompts. It’s asking you what you want and then doing its best to match that.” Agentic AI “can ingest a request, and then it is empowered to do its own thing with that.” The clue, she said, is in the name: it has agency. For a deeper technical comparison, see IBM’s explainer on agentic AI vs. generative AI.

That last row is the whole story. A bad draft costs you a few minutes of editing. A bad action can reach attendees, sponsors or your data before anyone looks. So the move from generative to agentic is not mainly a technology upgrade. It is a decision about trust.

Agentic AI vs generative AI examples in event planning

The clearest way to see the difference is to line up real tasks, from pure drafting to full action. All five below came up at the Open House.

1. Polishing your writing (generative). Wendy Porter trained as a math major and doesn’t see herself as a natural writer. “But I know what I want to say,” she said. She gives ChatGPT her points and gets back tighter copy that still sounds like her. Nothing happens until she approves it. That is generative AI at its best.
2. Loading a thousand options (agentic, low stakes). Katie used to type every choice into a platform’s dropdown menu by hand. Now, through MCPs (Model Context Protocol connections that let an AI tool work inside other software), she can hand over a list and say, “here is an import list, please add these.” In her words, it is “literally put thing A into place B.” The AI is acting, not drafting, but there is no judgment involved.
3. Pulling details out of 50 places (agentic, medium stakes). Event details arrive from everywhere: the show owner’s prospectus, client emails, vendor messages. Today, Wendy said, her team has to “toggle around, you know, 50 places” to assemble the full picture. She wants agentic AI to scan those sources and drop the key facts into one project plan. Here the agent is choosing what counts as important, so a person needs to check what it pulled.
4. Picking the executive guest list (agentic, high stakes). For exclusive executive programs, Katie described feeding partly anonymized CRM data to AI and asking “who’s actually active, who’s actually given buying signals,” rather than who has the grandest title. It replaces sales-team lobbying with evidence. It also decides who gets a seat, which is why the final list should still be human-approved.
5. Choosing sessions for attendees (agentic, someone else’s stakes). Katie described a future in which an attendee’s AI works as a “contextual wallet.” It knows their preferences, scans the session catalog, picks the best sessions and books them. The agent now acts for someone else, and its choices affect speakers and sponsors who never agreed to its rules.

Notice the pattern. The technology barely changes from example 2 to example 5. What changes is how much judgment the AI exercises, and whose interests ride on it.

What can an AI agent do for an event team?

An AI agent for event management can handle any task where the steps are clear and a mistake is cheap to catch. Our rule: delegate by the decision, not by the task. Ask what judgment the job requires and who pays if the agent gets it wrong.

That gives three levels of trust:

1. No decisions: let the agent act. Imports, data entry, copying dates into a plan, reformatting a schedule. Katie loves these jobs for AI precisely because they are “the thing that doesn’t need my human curiosity.”
2. Decisions with internal stakes: let the agent recommend, then approve. Shortlisting VIP guests, proposing a schedule, filling a budget. The agent does the legwork; a person signs off. Wendy’s own test shows why. She gave AI her budget template and a client’s numbers, and it “didn’t actually do as well as I had hoped.” Recommendation mode caught that. Action mode would not have.
3. Decisions that affect other people: set the rules and audit the results. Anything that decides what attendees see or which sponsors get exposure. Here the agent’s rules matter more than any single output, so people write the rules and review the patterns.

There is a prerequisite under all three levels. “I don’t want to have the AI conversation with you if we haven’t already had the data conversation,” Katie said. That applies doubly to agents. A generative tool given messy data writes one bad paragraph. An agent given messy data acts on it, over and over, at speed.

Where should a human stay in the loop?

A human should stay in the loop wherever an agent’s choices shape someone else’s experience, money or access. For event teams, that means attendee personalization, sponsor exposure and anything that filters what people see.

Katie’s contextual-wallet example is where this gets real. An agent that picks your sessions sounds like a helpful new door into the event. But, she asked, “at what point does it stop being a door and start being a gatekeeper at the door?” If the agent learns that you skip sponsored content, it may stop showing you any. The attendee never decided that. The sponsor never agreed to it. And the organizer may never notice.

This is the most important open question in event technology, and we think vendors, InEvent included, owe planners clear answers. Big shows are already there: BizBash’s Dreamforce 2026 report shows how Salesforce put autonomous AI agents in front of attendees. Before you switch on any agentic feature, ask:

• What can it change without my approval?
• Can I see a log of every action it took, and undo them?
• What data can it read, and how is attendee data anonymized?
• What is it allowed to filter out, and who set those rules?

Wendy’s rule covers the rest: “anything it spits out, it needs human oversight and review.” Katie went further. She asked us to keep some friction on purpose, because “friction is how we learn.” Remove it from chores like data entry. Keep it in discovery and conversation, where the human value of an event lives.

The bottom line

Generative AI made event teams faster writers. Agentic AI can make them faster operators, but only if they decide, deliberately, what it may do alone. Start with the no-decision jobs, keep approval on the rest, and treat any agent that acts for attendees or sponsors as a policy question, not a feature toggle. For how planners are using AI day to day, read our recap of the Open House: AI for event planning, narrative vs. reality.

Frequently asked questions

1. What is the main difference between agentic AI and generative AI?

Generative AI creates content in response to a prompt and waits for you. Agentic AI takes a goal, plans its own steps and acts on them, often inside other software. The practical difference is that generative AI produces drafts, while agentic AI produces actions.

2. What are examples of agentic AI vs generative AI in event planning?

Writing a session description or polishing an email is generative. Importing a list of options into an event platform, pulling details from many emails into one project plan, or shortlisting executive guests from CRM data is agentic, because the AI acts rather than just drafts.

3. Is agentic AI safe to use for events?

It is safe for tasks with no judgment involved and clean data. For decisions that affect attendees or sponsors, keep a person approving the output, check the agent’s action log, and agree on data and privacy rules with clients first.