What is Agentic AI? + How to start using it

I’ve been covering AI developments for almost a decade now, and the idea of AI tools that can be trusted to operate independently has always been exciting—and just out of reach. Agentic AI is the latest attempt to create a framework for what these tools would look like and how they could safely operate.

Here, we’ll explore what agentic AI is, how it works, and some real-world examples of agentic AI workflows you can start experimenting with today.

Table of contents:

What is agentic AI?

While the concept sounds very much like AI agents, agentic AI refers to systems of multiple AI agents collaborating to achieve complex goals. That distinction matters: a single AI agent might automate a narrow task in a workflow, but agentic AI commands multiple agents so your system can perceive what’s happening and coordinate action across a broad range of tasks.

What actually makes a system agentic?

Things get complicated because, as with all potential marketing terms, there’s a rush to call everything agentic AI—whether it really clears the bar for autonomous action or not. 

Imagine an AI email app that automatically screens your incoming emails. Is that an agentic AI system, an AI email agent, or just a fancy set of filters? It totally depends on what it can do without your intervention.

​Doing things like this would push it into agentic AI territory:

• Replying to emails without your input (within guardrails you set)

• Adding events to your calendar based on message content

• Unsubscribing you from recurring newsletters you never open

• Escalating certain senders or topics to a human with added context

If your “AI email app” can reliably take actions like these on its own, it’s much closer to an agentic AI system than a simple filter. But if it’s just sorting emails into folders, it sits in that awkward gray area where the marketing team might call it agentic AI, even if it doesn’t quite meet the definition.

Don’t get me wrong—agentic AI represents some big advances. It’s frequently called a “paradigm shift,” and enterprises are moving quickly on it, with 84% of leaders saying it’s likely or certain they’ll increase AI agent investments over the next 12 months.

How agentic AI works

Agentic AI systems are composed of multiple AI agents collaborating to manage complex tasks and achieve high-level goals with minimal human intervention. These systems have broad autonomy as to how they pursue their goals. 

Once you set one up, it should operate largely on its own, so it needs to be able to learn and adapt from its experience, as well as store and remember relevant information. As a result, these systems are able to manage dynamic and large-scale workflows. To take on the major tasks they’re capable of, they need access to large amounts of data and key systems they can use autonomously. 

Agentic AI systems tackle problems in a four-step process: perceiving, reasoning, acting, and learning.

Step 1: Perceive

You need to give your agentic AI system eyes and ears across your tools. Agentic AI systems perceive their environment by incorporating data from APIs, databases, external sensors, and user-entered prompts. This is how they know what goal they’re trying to reach.

In a typical Zapier setup, that might mean watching your help desk, CRM, billing platform, and internal databases so the system can pick up new tickets or leads as soon as they happen. You can start with a Zap that triggers when you receive a new support ticket or form submission, then hand it off to a Zapier Agent for triage.

Step 2: Reason

The system now needs to decide what it’s going to do next. An AI model, typically an LLM, takes the information the agentic AI system has about its goal and its knowledge of the tools and subsystems it has available, and it comes up with a plan. This can require pulling in more information using processes like retrieval-augmented generation (RAG), or deploying other, more specialized AI models to process images, read PDFs and documents, generate content, and the like.

For enterprises, this reasoning step is often where they bake in business logic and guardrails, like routing customer issues differently for VIP accounts or enforcing refund policies. It’s no surprise that human review is still a common management pattern.

Step 3: Act

Once it has a plan, the agentic system turns that plan into real work done. It takes action using the agents and other tools it has available—typically through APIs, though there are now dedicated protocols like Anthropic’s Model Context Protocol (MCP) and Google’s Agent2Agent (A2A) protocol.

Step 4: Learn

Learning separates a one-off automation from something truly agentic. If the agentic system was successful in reaching its goal, it should be willing to follow the same strategy next time rather than trying something totally new. Similarly, if it totally failed at achieving its goal, it should try a different tack. 

Right now, some enterprises try this step by layering in explicit feedback loops (like human approval or tracking conversions) so the team can update info over time.

Types of agentic AI systems

Not every agentic AI system looks the same. Most enterprises end up with systems that either span teams (horizontal) or go deep into a single function (vertical). Here’s how they differ.

Horizontal multi-agent

Horizontal multi-agent systems spread agents across multiple departments or domains, all coordinated toward shared goals. Maybe that’s one agent handling customer support triage, another enriching CRM data, another generating reports, and a fourth coordinating handoffs between the first three.

In my experience, these systems shine when you need end-to-end, cross-tool workflows, such as going from an inbound lead to a closed deal and an onboarding sequence. Agents can pass context along the chain and apply the same logic everywhere, while humans still step in at key approval points.

Zapier can easily orchestrate horizontal multi-agent systems, linking agents across multiple teams and tools with consistent, secure workflows that keep everyone aligned.

Vertical multi-agent

Vertical multi-agent systems go deep within a single domain or team, with multiple specialized agents handling different parts of one complex workflow. 

Say your recruiting team has one agent parsing resumes, another checking credentials, a third generating candidate summaries, and one more coordinating interviews. This aligns with how many enterprises experiment today: they start with one high-impact function and layer in more specialized agents over time.

Zapier is just as well-suited for vertical multi-agent systems. Zapier Agents can live right inside the workflows a single team already uses and connect deeply with its apps and data. You can give an operations team its own stack of agents for inventory, reporting, and incident response, all running on top of the same secure Zapier infrastructure that also supports cross-department use cases.

Agentic AI vs. generative AI vs. AI agents

To really grasp agentic AI, we need to wind the clocks back to early 2023. 

ChatGPT had launched a few months prior, and it was taking the world by storm. The large language model (LLM) that powered it, GPT-3.5, was a revelation. You typed in a prompt, and ChatGPT would generate a response that, at least 70% of the time, was shockingly good. The big catch? It was only trained on data up until September 2021. 

ChatGPT could regale you with information about rose horticulture and the Roman Empire, but it couldn’t tell you what the weather was like. If you asked it a question that fell outside its training data, it would respond with something like, “I’m sorry, but I don’t have information on that. My knowledge cutoff is September 2021.”

This early version of ChatGPT was purely a generative AI platform. The chatbot was able to take no action except to generate a response to your prompt based on its training data. It was a massive advancement in technology and had some uses, but it was pretty constrained in what it could do.

Eventually, we got different types of AI agents. These are AI tools that can act autonomously to perform well-defined tasks. The lowest possible bar for an AI agent is something like web search in ChatGPT. Now, if you ask it a question that falls outside its training data, it can decide to search the web for an answer and perform that search. It’s able to engage with the outside world in a limited manner. 

Of course, most AI agents aim to do far more than summarize a few Bing search results, but the key principles are the same. AI agents are autonomous (can act with minimal human intervention), task-specific (work on narrow, well-defined tasks), and reactive (can respond to changes). 

But AI agents are still pretty constrained. While they can act on their own to perform a well-defined task, they can’t pursue larger goals, remember key details, or learn from their mistakes. An AI agent can sort customer queries, fix a few bugs, or update your website—but a single AI agent can’t do all three well.

An agentic AI system, however, could take bug reports from customers, decide to fix and update your codebase, and publish the change log to your WordPress site. Whether you’d be wise to let one do it is another question, but we’re quickly reaching the point where it’s technically possible. 20% of enterprise leaders Zapier surveyed said that their AI systems operate autonomously with minimal oversight—so that future may be nearer than it feels.

Examples of agentic AI workflows

To show you what agentic AI workflows look like in the real world, here are a few examples. Some are hypothetical (but completely possible), and some are based on actual workflows created with an agentic AI system on Zapier.

Agentic AI workflows in customer service

Let’s consider an agentic AI customer service system that, among other things, can issue refunds when someone fails to cancel a trial on time. It would break down like this.

• Perceive: The agent receives new customer tickets and decides what to do with each one. In this case, it’s a ticket from a customer who forgot to cancel their trial.

• Reason: The agent looks at the information in the ticket and checks the customer database to see if the trial date and payment date make sense. 

• Act: If the agent thinks that the customer is due a refund, it uses Stripe’s API to issue one. If the agent thinks the customer isn’t due one, it replies and asks if they want to cancel their plan instead.

• Learn: Every week, a human customer service rep goes through all the tickets and gives them a thumbs up or a thumbs down.

Agentic AI workflows for bug fixing

Now let’s consider something a little more powerful: an AI coding agent that’s tasked to fix bugs.

• Perceive: The agentic AI has access to the codebase, the server logs, and the bug report database. If the server logs show an error or it receives a bug report from a customer or internal user, it takes action. 

• Reason: The agentic system uses an LLM to consider the bug report, searches through the codebase to find the problem, and comes up with a solution. It may also need to pull in information from internal databases, external help documents, and even ask a software developer for more information.

• Act: Once the agentic system has identified a potential fix, it tests it in a dedicated local environment. If it works, it submits a merge request to GitHub for a software developer to review. If it fails, it considers the error message and tries to create a new fix.

• Learn: The agent sees whether its merge requests are approved or rejected and also learns from the error messages from testing its own code fixes.

Agentic AI workflows for automatic sales follow-ups

NisonCo used Zapier Agents to follow up on sales calls, delegating an otherwise complex system to the agentic AI. The perceive, reason, and act steps are actually in action here—the learn step would bring it fully into agentic AI.

• Perceive: The agentic AI receives call recordings from the sales team and transcribes them.

• Reason: The agentic system uses an LLM to scan the transcript and pull out the prospect’s details and any action items. 

• Act: Once the agentic system has identified the action items, it generates a draft email attaching any documents, prospectuses, and files necessary. It also logs all the relevant details in the CRM.

• Learn: The agent sees whether the deal closes in the CRM and also learns whether the email it drafted gets sent or not.

Agentic AI workflows for assessing potential hires

JBGoodwin Realtors similarly built an agentic AI system to create a dossier on potential hires when they were overwhelmed with applications. Same thing here—the learn part isn’t part of the actual workflow, but it’s still a complex agentic system.

• Perceive: The agentic AI receives job applications. 

• Reason: The agentic system uses an LLM to parse the job application and see if they meet the job criteria. 

• Act: If the AI determines the potential applicant meets the job criteria, it connects to a professional registry to check licensing details, pulls in a job history from LinkedIn, searches Google for any relevant personal or professional details, and creates a summary of its findings that it emails to a recruiter—along with the attached resume. It also calculates a hireability score.

• Learn: The agent learns which candidates are hired or not and uses its hireability assessments as a measure of how well it assessed each candidate. If it scores candidates incorrectly, it updates its criteria.

Agentic AI workflows for lead generation and outreach

UK clean energy brand egg built an agentic lead generation and outreach system on Zapier. Same thing here about stopping just short of learning.

• Perceive: The agentic AI has access to the CRM, inbound marketing channels, and feeds or databases with potential leads. 

• Reason: The agentic system uses an LLM to assess each inbound lead. It also scans the feed or database for new potential leads on a daily basis.

• Act: Once the agentic system has identified a potential lead, it adds it to the CRM, enriches the data using Google and other search tools, and sends an initial outreach email. It also analyzes sentiment in replies to its outreach and sends a notification for negative feedback or creates a HubSpot lead for positive feedback.

• Learn: The agent sees the conversion rate of its identified leads to hone its system.

Challenges of agentic AI

For all the excitement, agentic AI also adds new challenges around control, safety, and operations that companies are still figuring out.

• Oversight and control: Agents that get more and more autonomous might also be harder to understand when it comes to their decision-making. It could also get tough to guarantee they’ll always stay within policy. This makes explicit approval steps a crucial part of any agentic workflow.

• Security and data privacy: Agentic systems often touch sensitive customer records, financial data, or proprietary code. Zapier found that 18% of enterprise leaders say security and data privacy limit their AI agent adoption, which makes enterprise-grade controls like role-based access and audit logs non-negotiable.

• Complexity and maintenance: Coordinating multiple agents across apps and APIs makes systems harder to debug and maintain. A small change to one tool or prompt can have ripple effects downstream. Many teams are responding by starting with narrow, deterministic workflows that include AI steps—using solutions like AI by Zapier—before gradually layering in more autonomy.

Even after you tackle these challenges and the tech is ready, your teams might not be. People need to trust agentic systems before they’ll rely on them for critical tasks. Zapier lets you implement measures like human in the loop (HITL) to pause automation for review, so your team knows they can step in at key points to make changes if needed.

Getting started with agentic AI

Right now, true agentic AI falls just out of reach in most instances. While it’s possible to build powerful agentic systems that combine multiple autonomous agents with tools like Zapier Agents, making them able to learn automatically from their actions and safe enough to work unsupervised requires a deep understanding of AI and the trade-offs you’re making. Giving any AI tool full access to all your company’s data or production server is still risky.

But even if you don’t plan on handing all of your operations over to AI, Zapier Agents is one of the best ways to start building agentic systems. As things advance, you can be sure it will incorporate the features necessary to build fully autonomous, self-learning agentic AIs. Learn more about how to build with Zapier Agents, or get started for free.

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This article was originally published in June 2025. The most recent update, with contributions from Mike Floeck, was published in March 2026.