AI Agents in Marketing: What they are, how to use them, and what to expect

AI Agents in Marketing: What they are, how to use them, and what to expect

Artificial intelligence has moved quickly from a rather abstract concept to an everyday tool, particularly in marketing. While many small and medium-sized businesses are already using AI in some way, most often through content tools like ChatGPT or analytics plugins, a new and more powerful class of AI systems is emerging: AI agents.

AI agents are being talked about more and more in business and tech circles, but in my experience most people are still not sure what they actually are. The term sounds technical, possibly futuristic, and a bit ambiguous.

To strip it back, an AI agent is quite simply a system that can carry out tasks on your behalf, using reasoning, decision-making and access to tools. So rather than waiting for you to prompt it whenever you want something done, an AI agent can follow a goal, break it into steps, decide which action to take next, and adjust based on the results it gets. In short, it doesn’t just respond, it acts. A bit like a competent junior assistant, but without coffee and fewer questions.

This is a significant change in how we think about automation. OpenAI's own guide to building agents describes it as agents being designed not just to answer questions or write content, but to manage whole workflows. That might be anything from booking a series of meetings and following up by email, to running a social campaign or handling a multi-step customer service interaction. The key difference is that AI agents can execute these workflows independently, within the rules they are given.

So how does this apply to marketing?

If you're running or working in a small business, you're probably doing more than one job at once. You're planning campaigns, analysing results, writing content, updating your website, replying to customer queries, and you're managing ad spend. All of these tasks require different systems, different types of thinking and a fair amount of repetition. That's where agents can be added to help.

For example, imagine you need to run a product launch. A basic AI tool might write a few headlines or suggest some hashtags. An agent, on the other hand, could draft a launch plan, schedule the emails, write and post social media updates, check in on campaign performance after a few days, tweak what isn’t working and then summarise the results for you. Crucially, it wouldn't need to be prompted to move from one task to the next, you simply give it a goal and it would run with it, reporting back to you as needed and/or requested.

At the heart of an agent is a large language model (LLM) like GPT-4, which is the engine that allows it to reason, to interpret information and to decide what to do next. But to be useful, it also needs tools. These tools might include access to your CRM, your email platform, your website CMS or third-party apps like Canva or Buffer. The agent uses these tools to gather information or take action, depending on what the workflow requires. The third part of the system is instructions. These define what the agent should do, what it should not do, and what to do in cases when something unexpected comes up.

As OpenAI’s guide explains, not every task needs an agent. Some jobs are better handled by simple automation or rules-based systems., however agents do shine when the work involves nuance, judgement or a lot of unstructured information. In scenarios like reviewing marketing copy to make sure it matches tone of voice, prioritising leads based on message content or adapting a campaign in response to real-time engagement data, these tasks don’t follow a strict decision tree and so they require something closer to how a person might approach them. That’s where the intelligence part of AI agents comes into play.

At the moment, AI agents are already proving useful to small teams and solo marketers. Freelancers use them to manage tasks like client onboarding, automate reporting or handle simple research. Small e-commerce brands are using them to monitor product reviews, summarise customer sentiment and trigger email responses. Agencies are testing them in internal tools to generate weekly reports, suggest campaign optimisations and write up meeting notes.

It’s still early days. Most of the examples in use today are single-agent systems, so they are relatively simple to set up, especially with tools like the OpenAI Agents SDK. You give the agent a specific role, a set of tools it can use, and a clear goal. The agent runs in a loop until it either finishes the job, encounters an error or hands back to you. Over time, this single-agent setup can grow and you might want to add more tools, refine your instructions or even start combining agents into multi-agent systems. In these more complex setups, one agent might act as a manager, coordinating a set of specialist agents to handle different parts of the task.

As complexity increases, so does the need for guardrails. Agents are autonomous, but they should not be uncontrolled. The guide from OpenAI includes detailed advice on setting limits, detecting when an agent is straying off course, and making sure human oversight is available when needed. This might include content moderation, privacy filters, approval checks before certain actions, or simply setting thresholds that trigger human intervention if the agent fails too many times.

Looking ahead, it seems likely that agents will become more common in marketing tech stacks. Rather than being limited to developers or enterprise teams, they will start to appear inside tools that SMBs already use. You might log into your CRM and see a suggestion from your agent, based on something it has noticed in the last week. Or your website editor might prompt you with changes based on performance and user behaviour. In some platforms, this is already starting to happen.

The long-term shift is from prompting AI to partnering with it. Instead of telling it what to do every time, you begin to delegate. And like any team member, the better the agent understands your business, your brand and your goals, the more valuable it becomes. The opportunity for small businesses is significant, because rather than hiring more people or working longer hours, agents offer a new way to scale intelligently.

Of course, not every business is ready to adopt AI agents overnight. There are questions to consider around data privacy, accuracy, oversight and brand integrity. These are manageable with clear instructions and the right checks in place.

If you're exploring how to make your marketing smarter, faster and more scalable, it's worth taking a closer look. The guide from OpenAI is aimed at developers, but the principles apply to marketing teams too. Start by identifying the repetitive, complex or hard-to-automate tasks in your workflow, which is where an agent might be most helpful.

You don’t need to build a full multi-agent architecture or learn to code, you can begin with one use case, one outcome and a bit of curiosity. The technology is evolving rapidly, but the core idea is simple: intelligent systems that work with you, not just for you.

For small businesses, that might be the most exciting shift of all.