4 Must-Know Questions About AI Agents Every Advertiser Should Ask in 2025

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1. What’s the Difference Between a Chatbot, a Copilot, and an AI Agent?

Chatbots, copilots, and AI agents all provide automated assistance, but they differ in sophistication and autonomy. It helps to think of them as stages in the evolution of AI assistants:

Chatbots – Rule-Based Responders

The most basic chatbots simulate conversation in a limited way. They typically follow pre-written scripts or decision trees.  

Use case: answering FAQs on a website. They’re effective for simple Q&A but struggle with nuance or complex requests. Essentially, a chatbot will only handle exactly what it’s programmed for – anything outside that, and it gets confused.

Copilots – Intelligent Assistants:  

Copilots (a term popularized by tools like GitHub Copilot or Microsoft’s AI assistants) are a step up. They use more advanced AI, often large language models, to provide contextual help and suggestions within applications. A copilot can observe what you’re doing and offer guidance.  

Use case: in advertising, a copilot might draft an email subject line or suggest keywords while you build a campaign. Copilots streamline workflows, but they still require a human in the driver’s seat to make the final decisions.

AI Agents – Autonomous Problem-Solvers

An AI agent goes further by operating independently towards a goal. Agents don’t just suggest actions, they take actions on your behalf. They leverage advanced AI (machine learning, NLP, etc.) to perceive their environment, make decisions, and execute tasks without needing step-by-step instructions. In other words, an AI agent is more like a virtual team member than a tool.  

Example: Instead of merely recommending budget changes, an agent could autonomously reallocate your ad spend across channels in real time based on performance data.

In short, chatbots = simple Q&A bots; copilots = smart assistants that help you work; AI agents = truly autonomous actors that can achieve goals. This evolution from chatbot to agent has been called the journey into “the age of agentic AI,” as businesses shift from just getting insights to letting AI drive workflows.

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2. How Do AI Agents Work Under the Hood (And What Makes Them “Autonomous”)?

AI agents are powered by a combination of advanced AI techniques that allow them to observe, decide, and act on their own. At a high level, here’s how an autonomous agent works:

Brain of an LLM

Most modern AI agents leverage large language models or similar AI models as their “brain.” This gives them natural language understanding and generation capabilities. In practice, that means an agent can interpret complex instructions (e.g., “Optimize my campaign for maximum ROI”) and break them down into actions.

Memory and Context

Unlike basic bots, agents maintain contextual memory. They recall past interactions and relevant data when making decisions. This could be short-term (within one session) or long-term (persisting knowledge about your marketing and advertising strategy or brand guidelines). This awareness lets agents make informed decisions rather than one-off responses.

Tools and External Data (RAG)

Crucially, AI agents aren’t limited to pre-trained knowledge. They use techniques like Retrieval-Augmented Generation (RAG) to pull in up-to-date information. They can connect to databases, APIs, or the web – for example, querying today’s campaign stats or fetching a product detail – and incorporate that into their reasoning. They also can invoke tools or functions: an agent might trigger an analytics report, send an email, or adjust a bid via API. This ability to call external tools is what allows agents to act in the real world beyond just chatting.

Goals and Decision Engine

Every agent is given a goal or role (e.g., “maximize conversions for campaign X under budget Y”). Within its programming, it has decision-making logic – often using AI planning algorithms or reinforcement learning – to figure out the best sequence of actions to reach the goal. The agent evaluates options, monitors results, and can adjust its strategy on the fly. It operates with a degree of autonomy, meaning it doesn’t need a human to okay every minor tweak.

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3. How Is It Possible to Identify a Real Agent From a Disguised AI Assistant?

One framework used to recognize a truly autonomous agent is the “5 A’s” – Awareness, Analysis, Autonomy, Action, Adaptability. In simple terms, a good AI agent is aware of context (knows the campaign history, targets, etc.), analyzes data to inform moves, exercises autonomy to make decisions, acts by executing tasks (posting updates, reallocating budget, generating content, etc.), and adapts by learning from feedback (improving its strategy over time). These capabilities let agents function as self-directed entities rather than passive tools.

In essence, an AI agent is like a digital worker: it has the knowledge (via AI models and data access), the skills (via tool integrations and algorithms), and the decision-making capacity to carry out marketing or advertising tasks with minimal hand-holding. This autonomy is what distinguishes agents – for example, an AI Agent might autonomously pause an underperforming ad campaign at 3 AM (while you’re asleep), because it learned that certain spend was being wasted, and then shift that budget to a better channel. All of this happens because the agent was entrusted with a goal and the means to act on it.

4. What Is a Multi-Agent System, and Why Are More Companies Using Teams of Agents Instead of Just One?

If one AI agent is helpful, imagine a team of specialized agents working together – that’s a Multi-Agent System (MAS). In a MAS, you have multiple agents collaborating (and sometimes negotiating with each other) to achieve bigger objectives. Why go multi-agent in media and advertising? Here are a few reasons:

Complex, Multi-Step Tasks

Some challenges in advertising are too intricate for a single agent to handle efficiently. For instance, launching a multi-channel campaign involves strategy planning, audience research, budget optimization, and real-time adjustments. A single agent trying to juggle all these tasks might get overwhelmed or perform suboptimally. MAS allows you to split a complex workflow among agents, each tackling a part. One agent might specialize in strategical analysis while another focuses on media planning, and another manages bidding – all working in concert.

Specialization (Experts for Each Role)

In marketing teams, you have specialists (the media buyer, the data analyst, the creative, etc.). Similarly, in a MAS each agent can be an expert in a specific domain. For example, you could have a “Data Analyst Agent” and a “Budget Optimizer Agent.” By dividing the roles, each agent can use highly tuned algorithms for its niche, which boosts overall performance. This specialization is a big benefit: an agent trained to optimize bids can do so while another agent trained in NLP writes better ad copy – together they achieve what one generalist agent cannot.

Collaboration and Parallelism

Multiple agents can work in parallel, speeding up processes. While one agent is crunching historical data to forecast optimal spend, another simultaneously generates dozens of ad variants for testing. They then share results. This distributed approach means MAS can tackle large-scale problems faster and more robustly than a lone agent. In fact, companies like MINT have introduced frameworks to orchestrate multiple agents at scale to execute multi-step workflows efficiently.

Tool/Task Overload

Oftentimes a single agent with too many tools or responsibilities can get bogged down. Think of an agent that has 50 different APIs it could call – deciding which tool to use becomes a task in itself. MAS can mitigate “tool overload” by assigning different tools to different agents, so each agent has a clearer, simpler toolkit. This makes each agent’s decision process more focused.

A Multi-Agent System basically functions like an orchestra of AI agents with some kind of coordination mechanism. There are a couple common ways they coordinate. One approach is a “Project Manager” Agent that delegates tasks to others and then consolidates the results. Another approach is a sequential or pipeline flow, where one agent’s output feeds the next agent in a predetermined chain file – for example, Agent A does research, passes it to Agent B who plans strategy, then to Agent C who executes the campaign.

The main idea is that a Multi-Agent System structure and teamwork among agents, which becomes increasingly important as you attempt to automate bigger portions of the advertising workflow. Instead of one agent trying to do it all, you have an army of agents each doing what they do best – and the whole is greater than the sum of its parts.

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See You in Part II

From how they think to how they work together, AI agents are already reshaping the foundations of digital advertising. But what about real-world use cases, industry success stories, and how to safely adopt these tools in your own team?

In Part II, we’ll dive into the next 4 questions every CMO and media leader should ask—covering what agents are doing in the wild and what it takes to get started.