You’ve probably used tools that follow a strict, step-by-step routine: download the report, filter the data, copy it into a spreadsheet, and repeat. But what if you could tell a software, “Here’s my goal. You figure out the rest,” and it does end up figuring out the rest?
That’s exactly where AI agents step into the picture! AI agents are software programs that work autonomously and are output oriented in nature. That means you tell them what you want to know, and the program figures out a way to do it on its own.
Unlike chatbots (that just respond to prompts) or automation tools (that execute fixed sequences), agents actively use LLMs to reason about problems and coordinate multiple tools to reach your desired outcome.
Sounds complicated? Don’t worry! In this blog, I will explain what is an AI agent and How do AI agents work at backend to actually complete the task assigned. Let’s dive right in!
An AI agent is an autonomous software program that can take decisions on its own, and then act on those decisions to give the right output to the user.
So basically, an AI agent doesn’t just follow a given set of instruction, it figures out what to do next. It senses inputs (text, data, images), reasons using models or built‑in logic, and then executes actions. These actions can be anything — like sending emails, updating records, trading stocks, or any other thing that normally would require human intervention.
Now, you might say that sounds a lot like an AI workflow, and I’d say you’re not wrong. But there are certain differences between both.
An AI workflow runs on a predefined sequence of steps, and cannot improvise or adapt according to the requirements.
For example: if you’ve set an AI workflow to email you a performance report at 8 AM daily, that’s all it is going to do. An AI agent, however, could also be instructed to review that report and take appropriate action accordingly.
Generative AI, in contrast, is all about creating new content—drafting text, images, or code—based on learned patterns. An AI agent might leverage a generative model as one of its tools, but it combines that creativity with goal‑driven planning, memory of past actions, and integration with external services.
In short, an AI agent blends perception, planning, and action into a self‑driving loop—your proactive, adaptable digital partner.
Now that you know what is an AI agent, let’s understand how do they work at the backend. Or more importantly, what sequence of tasks or actions is happening to give you the desired final output.
At the heart of every AI agent lies a simple loop: sense → think → plan → act.
1. Sense: The agent collects information from its environment. This could be your text message asking a question, data from a website, or information from connected systems like your calendar or email.
2. Think: Using its “brain” (typically a large language model or LLM), the agent processes this information. It considers what it already knows about you and the task, and remembers previous conversations.
3. Plan: The agent maps out a strategy to achieve your goal. It determines which steps to take, what information is needed, and which tools to use along the way.
4. Act: The agent takes concrete actions based on its plan. This often involves “tool calling”—where the agent activates specific functions or external services to accomplish tasks. It might search for information, write a message, update a spreadsheet, or access specialized APIs.
What makes this powerful is that the cycle repeats automatically. After taking an action, the agent observes the results, thinks again, adjusts its plan if needed, and decides what to do next, and you don’t have to guide its each step.
💡 What is Tool Calling? Tool calling is when an AI agent reaches out to outside services or programs to get work done. For example, it might ask a weather API for the latest forecast or connect to your calendar app to book a meeting. The agent decides which service to use, sends the request, and then uses the response to move forward in its task.
Now that we understand what AI agents are and how they operate, let’s break down the key components that make up an effective AI agent system. These building blocks work together to create that seamless sense → think → plan → act loop we discussed earlier.
It can be hard to distinguish between a regular AI tool and an AI agent. But here are some characteristics you should be actively looking for when classifying an AI tool as an AI agent.
An AI agent doesn’t wait for step-by-step commands. Give it a goal, and it takes the initiative to figure out what to do next on its own.
Tools often run once and stop. Agents keep context across sessions, picking up where they left off and handling long-running tasks.
Agents aren’t just reactive, instead they actively pursue outcomes. They evaluate options, anticipate obstacles, and adjust their plan to stay on track.
While basic tools handle single tasks, agents chain multiple logical steps together. They break complex problems into bite-sized actions. So basically, instead of a single query, an AI agent will run multiple queries in a sequence.
True agents coordinate across several services—APIs, databases, generative models—choosing the right tool for each subtask.
AI agents can be sorted by what they can do and how complex they are. These different agents allow for custom solutions in many fields. They show how flexible AI can be.
Here are some common types of AI Agents:
These agents are designed to excel at particular tasks or within defined domains. They have deep specialization rather than broad capabilities.
Examples:
These agents focus on supporting individual users across various aspects of their digital lives, learning preferences over time to provide increasingly personalized assistance.
Examples:
These agents work largely independently, monitoring systems and taking action when needed with minimal human oversight.
Examples:
These agents work together as part of a larger system, each handling different aspects of complex problems.
Examples:
These combine the structure of traditional workflows with the adaptability of agents, following general patterns while adjusting specific steps as needed.
Examples:
In business functions, AI agents have multiple use cases. And one such field that’s getting majorly impacted is growth marketing.
Growth marketers have traditionally juggled numerous tools, platforms, and datasets while trying to optimize the customer journey at every touchpoint. But with AI agents in the picture now, basic tasks can be automated, leaving more space for creative and strategic tasks.
Let’s look at how AI agents are showing up in the day-to-day work of growth teams:
Traditional A/B testing requires marketers to manually set up experiments, wait for results, and then implement changes.
AI agents can continuously generate and test dozens of creative variations simultaneously across different channels. They autonomously analyze performance data, identify winning combinations, and automatically shift budget toward high-performing assets—all without requiring a marketer to monitor each test.
Instead of pushing customers through predetermined funnels, AI agents can orchestrate truly personalized journeys. These agents track individual engagement across touchpoints, predict likely next actions, and dynamically serve the most relevant content or offer at precisely the right moment.
AI agents continuously learn from each interaction, refining their understanding of what drives conversions for different customer types and automatically adjusting messaging and timing to maximize results.
Traditional analytics workflows require a marketing analyst to sit and manually spot data patters and extract insights. That means they have to go through the Ads Manager, export data in a spreadsheet, and extract information that can be used to decide the next course of action.
Now, let’s look at another angle where AI agents can process vast datasets to uncover hidden correlations and opportunities. These agents can fetch data from certain sources (for example: advertising platforms), identify complex patterns human analysts might miss, and then translate those findings into actionable recommendations with clear business implications.
For example: consider Vaizle. Vaizle’s smart chatbot is an AI agent for Meta Ads that fetches your ad account data from Ads Manager, understands it, and then answers anything you might want to know.
So, if you want to figure out the performance of a particular adset, all you need to do is ask a question. Type “Calculate shift in gender adspends this week as compared to last week” and you will get the answer without having to go through multiple reports or dashboards.
And that’s the beauty of AI agents in action. You don’t have to manually repeat processes or follow lengthy workflows that lead to the final answer.
To wrap things up, let’s first quickly go through what we’ve learned so far.
TL;DR
Now that you know sufficiently about AI agents, it is time to figure out how you can integrate agentic AI in your workflow.
If you’re a performance marketer (who runs Meta Ads or is planning to start), Vaizle AI is a good starting point. With a $1 trial, you will be able to explore how your actual ad analysis time can drop and how you can spot data patterns that are usually easy to miss!
And the best part is that, starting out is really easy! Simply sign up, connect your Meta Ads account, & start a chat.
Purva is part of the content team at Vaizle, where she focuses on delivering insightful and engaging content. When not chronically online, you will find her taking long walks, adding another book to her TBR list, or watching rom-coms.
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