What is Agentic AI? And how you can start using it

What is Agentic AI? And how you can start using it

Agentic AI is a system made up of multiple, connected AI agents working together to autonomously carry out complex, multi-step goals with minimal human involvement.

Agentic AI is the latest attempt to create a framework for what AI tools would look like and how they could safely operate.

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

What is agentic AI?

While the concept sounds very much like AI agents, there are more differences than just the word order. The basic distinction is that AI agents are capable of acting on their own to perform specific tasks within narrow guardrails, while agentic AI refers to systems of multiple AI agents collaborating to achieve complex goals.

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. If it can reply to emails without your input, add events to your calendar, and unsubscribe you from annoying email newsletters, that counts as agentic AI, but just sorting emails falls into that awkward gray area where the marketing team will probably call it agentic AI, but it doesn't quite meet the definition. There could well be an AI agent using a large language model (LLM) to determine where to sort your email, or it could just be some Gmail filters.

Agentic AI represents some big advances. It's frequently called a "paradigm shift." But it's important to step back and look at what agentic AI systems really are, what their potential is, and how you can get started using them.

Agentic AI vs. generative AI vs. AI agents

To really grasp agentic AI, wind the clocks back to early 2023. ChatGPT had launched a few months prior, 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 generative AI. The chatbot was able to take no action except to generate a response to a 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, AI agents arrived. These are AI tools that are able to act on their own 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's able to decide to search the web for an answer, and it's able to go 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.

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.

This is where agentic AI gets interesting.

Agentic AI in action

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

Once you set one up, it should be able to operate largely on its own, so they need to be able to learn and adapt from their experience, as well as store and remember relevant information. As a result, they're able to manage dynamic and large-scale workflows.

Agentic AI systems have the capacity to take on major tasks, like supply chain management, business process optimization, application development, and project management. To do this, they need access to a large amount of data and key systems that they can use autonomously.

Agentic AI systems tackle problems in a four-step process:

• Step 1: Perceive. 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.
• Step 2: Reason. 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.
• Step 3: Act. Once it has a plan, the agentic system 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. An agentic AI system has some mechanism to learn. If it 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 reaching its goal, it should try a different tactic.

Examples of agentic AI workflows

Here are a few examples to show you what agentic AI workflows look like in the real world. Some are hypothetical (but completely possible) and some are based on actual workflows.

Agentic AI workflows in customer service

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 it a thumbs up or a thumbs down.

Agentic AI workflows for bug fixing

Now 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 followed 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 from 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

U.K. clean energy brand egg built an agentic lead generation and outreach system. 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 does sentiment analysis on replies to its outreach and sends a notification for negative feedback or creates a HubSpot lead if it's positive.
• Learn: The agent sees the conversion rate of its identified leads to hone its system.

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, 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.

This story was produced by Zapier and reviewed and distributed by Stacker.