Zapier Agents Masterclass

Everything you need to know about Zapier AI Agents.

Today, I’m sharing with you all that I’ve learned in the past year about Zapier Agents to spare you the hours it might take to learn these things firsthand.

Why is this so important?

I’ll hit a beat on the drum we’ve all been dancing to: AI is moving so fast that it’s hard to keep up. But we know it’s equally important to keep up.

Let me bring some relief: AI Agents are simpler than you think.

I’ll show you how to master Agents inside this post. Here's what we'll cover:

Who's this post for?
Zapier Agents 101
Agent use cases that work today (with templates)
How to think in Agents
How to build Agent systems
Tips, tricks, and gotchas
What’s next for Zapier Agents?
Wrapping things up

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1. Who’s this post for?

If you’re a builder who wants to learn and build AI Agents but wants to avoid wasting time, this post is for you.

I’ve included real examples with templates as well practical action steps and building ideas you can implement today.

Let’s do this!

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2. Zapier Agents 101

I have good news: building with Agents is simple. It’s the mental model and use cases for building Agents that can be novel and complex.

So, here’s a quick lightning lesson on Zapier Agents:

An Agent. Chat with an Agent and add data sources just like you would in ChatGPT but with the ability to add actions in any app you integrate, as well. The equivalent to a folder for a set of Zaps.

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Behaviors. Recurring work you define for the Agent to get done. The equivalent to one Zap.

Define triggers.
Write instructions.
Add actions.
Add data sources.

Triggers. They wake the Agent up. The equivalent to a Zap trigger.

Trigger from any app that integrates with Zapier (i.e. a new form entry, new lead, updated field, every Wed. at 9am, etc.)
Trigger on-demand by clicking a button (the equivalent of “forcing” a Zap to run or setting up a button in a Table to trigger a Zap)

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Instructions. Type out what you want the Agent to do. The equivalent to building a Zap but typed out, instead.

Typing out instructions is the main differentiator from other Agent platforms out there.
Typing out instructions offers one main advantage: Speed.
- Speed to build
- Speed to edit

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Instructions: Actions. Add actions from another app (i.e. send Slack DM, update record, etc.). The equivalent to an action step in a Zap.

The major difference from a Zap: letting the Agent decide what values to fill out for an action.
An Agent deciding on what values to fill out = Speed.

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Instructions: Data Sources. Add data sources for the Agent to reference and analyze when it gets to work.

Allow an Agent to see an entire data source and pull information from it.
Alternatively, use a search to find a specific set of data and then reference that subset. The equivalent to a Search step in a Zap paired with an AI step to analyze what was returned.

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There are a few other things to look at like a place to review activities taken (the equivalent to tasks in Zaps) and a Chrome Extension for use around the web (the Agent can see the webpage you’re visiting).

You’ll have noticed all the similarities to the traditional Zap. In fact, most competitor Agents are actually just following the same pattern Zapier’s already established with Zaps. They have a set of steps and simply add an AI step in the process.

You can do all that with a Zap.

But here’s what makes Zapier Agents different than the competition:

With Agents, you type out the entire instructions instead of having to define determined steps
With Agents, you let the Agent have more autonomy to make decisions:
- Agents decide when to use actions and data sources. You can give guidance but the Agent decides if and when it uses them to accomplish the goal.
- Agents decide when to use built-in tools like web search and analysis
With Agents, you can easily build full systems within Zapier using other built-in products like Tables, Interfaces, Chatbots, Canvas, and Zaps to give the Agent context and an environment to work in. This makes them more powerful.

We’ll dive in further and you’ll see these differences play out in real examples.

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3. Agent use cases that work today (with templates)

The key for coming up with Agent use cases: focus on AI’s unique abilities which are to analyze large sets of data in seconds and search the web for you.

In other words, start with the data.

Because no matter how long you look at 5,000 rows of data, you’re not going to be as good as AI at pulling out patterns.

So, with data in mind, here are my personal Agents that I actually use.

I’ll be showing you the behaviors I built but they could be housed within a single Agent or split up among multiple Agents as makes organizational sense to you.

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Communication and Content Agents

Use to help aid in daily communication and operations for my day-to-day work.

Video Slack Agent

Data: a Zapier table with all videos I’ve published along with metrics

Within Slack, all you have to do is mention @brycevideos and ask a question. The Agent will look at the video data source and return an answer.

Get the Slack Agent template.

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New Video Request Agent

Data: all of my published video transcripts and a task reference table listing out all my common tasks along with estimated time allotments.

When I get new video requests, I kick off this Agent. It add the requests to a tracking table, blocks of time for filming and editing based on my availability and the video due date, looks at my old video transcripts and drafts an outline for the new video request.

Get the Video Request Agent

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Call Prep Agent

Data: my calendar

Every evening, prepare me for meetings with external participants for the next day. Search the web so learn about the people I’m meeting with.

Grab the Meeting Prep Template

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Email Task Agent

Data: my calendar and a task reference table that houses estimated duration for my most common tasks

When I forward an email thread to my Agent’s email address, I tell it to either block off time, add reminders/tasks, or schedule meetings. It reads the full email thread for context and takes the appropriate action.

Grab the Email Task Agent.

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Customer and Marketing Intelligence Agents

These do research and reporting for me.

Youtube Insights Weekly Agent

Data: all published videos among our influencers with metrics.

Each week, the Agent sends me a report on which videos and influencers are performing well so that we can optimize ROI.

Grab the Youtube Insights Agent.

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Competitor Tracking and Reporting

Data: the web. Specifically, competitor updates.

This searches and stores Agent competitor updates into a Zapier table. If something is particularly important, the Agent sends me a Slack DM (I let the Agent decide on criteria).

Data: the competitor tracking table

Each week, it looks at the entirety of the table to spot trends and suggest counterstrategies.

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There are many other use cases that I don’t personally use in my day-to-day but have seen get real work done. Here are a couple more places you can check out:

Agent templates
AI marketing team (social ad reporter)

You can copy these or you can get your hands dirty and build yourself. To build them yourself, you need to understand how to think in Agents.

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4. How to think in Agents

You’ve been given $50k to hire someone. What would you have them do?

You’d probably need to delegate some assistant-level tasks like coordination, task management, and research. Things that are easier to do but still require time and mental energy.

That’s exactly where you should start with Agents.

The benefit you get from building an Agent instead of hiring an assistant is speed. And if you learn to build effectively, you also get massive amounts of leverage because an Agent never sleeps and can do multiple things at once.

When you delegate, decisions need to be understood.

So as a rule of thumb, as your Agents become more complex, lean more heavily on decision-making frameworks. That way when your Agent makes a decision you can understand why it made it.

Take a Feature Prioritization Agent. In order to make a decision about prioritizing feature requests, it needs:

Your strategy document
A decision framework for how to prioritize requests

The result is new customer feature requests prioritized with a written justification as to why.

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What you’ll notice from the Agent above is that it works in a system. There’s a request form, a table, a ticketing app, a strategy document, and an Agent.

Without a system, an Agent does not have context. But with AI, context is everything. So, learn to build systems.

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5. How to build Agent systems

Here’s exactly how to think about it.

First, remember that Agents go beyond chat. You’re working with existing apps, tools, software so while chat is a great way to get something done on-demand, if you want to scale you’ll need to build systems that work when you’re not around.

Pre-step: Canvas for system build

If you’re unsure of how to build what you need, I suggest going to Zapier Canvas and simply typing out the system you want.

Canvas will give you a great idea of how to use Zapier products in a system that can be referenced by an Agent later on in your build process.

If you already know exactly what you need, then you can come back to Canvas later to diagram what you’ve built so others can understand it.

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Step 1: Data with Tables and Zaps

Because Agents are most powerful with large amounts of data, you’ll get the most leverage by consolidating useful data into Zapier Tables.

Why Zapier Tables? Two reasons:

You can easily automate the process of consolidating data into Tables.
With Agents, Tables is an always updated data source—it syncs automatically when there’s an update.

This process could look like consolidating Facebook and LinkedIn ad data into one Table. You’ll need to set up Zaps to do this.

You can see how this works in the AI Marketing Team system.

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Step 2: Decide on deliverable

What do you want in the end? What’s the deliverable? Maybe it’s an email report. Maybe it’s a Google Doc with notes. Maybe it’s enriched fields in a table.

It could be multiple things but the point is to attack the problem from both ends:

Do this first: The backend: the data
The middle: the Agent
Decide on this second: The frontend: the deliverable

This step doesn’t take a lot of work, but it’s a decision that you need to make.

You wouldn’t give an employee a task without first giving them the context they need to get the job done and the final outcome you’re expecting, right?

Same is true for Agents. That’s why the Agent is the last step in the process.

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Step 3: Build the Agent

When you have the data and deliverable sorted out, now you can put the Agent to work.

Give it a trigger so it knows when to get to work. Then type out what it needs to do and what data it has to do it. Make sure it has access to make changes as needed and to send the final deliverable.

Now you’re off to the races.

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6. Tips, tricks, and gotchas

There are a few things worth mentioning that don’t necessarily fit into any of the previous sections but will help you as you build. So, here’s a list:

Only one trigger can be used for a single Agent behavior (just like Zaps can only have one trigger)
There’s no stored memory for a behavior, yet. If you want to track what your Agent does to be used by other Agents, then store actions in a Table as they happen.
Using delay or filter steps don’t work within an Agent. You must type out filters.
Formatting can be tricky sometimes based on the desired deliverable. Slack or Gmail messages don’t always look pretty.
Data sources take two form: data integrations AND data search actions. It’s confusing to understand. My rule of thumb: if you want to look at the entirety of a data set to draw conclusions use a data integration (indicated by a blue doc with lightning bolt). If you want to search and bring back a subset of data try using a search action like “Find records” and the like.
Using an Agent within a Zap is not possible, yet. Coming!
Having an Agent stop mid-behavior to ask you something, get approval, or pause for whatever reason is not possible, yet.

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7. What’s next for Zapier Agents?

This is everyone’s burning question. What will AI Agents look like in six months? A year?

It’s hard to say for sure, but here’s my take.

We’ll start to see Agents manage a swath of task-oriented Agents. So, you’ll interact with one Agent and it will serve as the switchboard for all the Agents you’ve built. It will use an each Agent as it sees fit.

This idea follows the same pattern we’ve seen with AI:

AI
AI with data
AI Agents with tools (web search, analysis, etc.)
AI Agents with MCP (app integrations and actions)
(future) AI Agents with Agents

The managing layer will rise and we’ll have Agents helping us manage Agents. Sounds wild, but that’s honestly where I see things going.

Imagine: you have a single Agent you interface with. You tell it you’re hoping to start a project. It helps you clarify all the necessary details and data it needs to push the work forward.

Then, it calls on the following agents: