We’ve seen a major leap in artificial intelligence (AI) technology over the last few years. While we used to only know simpler AI such as chatbots, facial recognition, and Google Maps, today we have more advanced AI with smarter and more complex functions. In the past, marketing tasks were done manually, but now more marketers are using AI to simplify their work. In fact, around 63% of surveyed professionals are planning to incorporate AI or Machine Learning (ML) in their influencer campaigns, meaning we’ll see more brands using AI for influencer marketing. To work efficiently, it’s important for marketers to understand which type of AI will help them the most.
Along with the speedy advancements in AI, types of AI are becoming more varied, and can sometimes be confusing to keep up. One example is the types we’re going to discuss today: AI agents and agentic AI. Recently, these terms have been all over tech news, and it’s possible some people think they’re just different ways of saying the same thing or even interchangeable. In reality, while they sound similar, AI agents and agentic AI are basically different, and this distinction can have a significant impact.
Understanding the difference is needed for brands to make well-considered decisions about which type of AI to adopt, as well as the potential benefits and risks they may face. In the end, this will affect how brands build relationships, generate results, and thrive in the long term.
Now, let’s take a closer look at AI agents and agentic AI, and then compare which one is better, safer, and more effective for brands.
What are AI agents and agentic AI?
Before deciding which one is best for your brand and marketing strategies, it’s important to understand what they are and what they’re capable of.
Let’s start with AI agents. An AI agent is a software program that acts autonomously to achieve specific goals by performing tasks on behalf of users or other systems. AI agents operate based on predefined rules or instructions and can interact with their environment to achieve the objectives. In real life, AI agents can be seen in the form of support chatbots, personal assistants, and recommendation systems. In the context of influencer marketing, any AI-powered influencer marketing platform that can detect fraud on social media handles is classified as an AI agent. Due to its functionality, as much as 82% of surveyed enterprises plan to integrate AI agents within the next three years.
On the other hand, you may have heard about agentic AI everywhere since the launch of Manus AI on March 6. Developed by a Chinese startup, it quickly grabbed the attention of AI enthusiasts due to its sophisticated features. It can independently plan, execute, and refine multiple tasks at the same time, even complex workflows such as data analysis, report generation, and web automation without needing constant user intervention. Manus is the most recent example of agentic AI, which many consider the software that marks the beginning of a new era in AI.
So, what exactly is agentic AI? In contrast to AI agents, agentic AI is an advanced AI system designed to autonomously make decisions, achieve complex goals with minimal human supervision, and adapt to changing environments. Unlike generative AI or AI agents which are more reactive to user input, agentic AI takes a proactive approach. This autonomous AI can independently learn and adapt to new situations without having to follow predefined rules.
An example of agentic AI is Darktrace, an AI cybersecurity company that uses agentic AI to autonomously detect, self-learn, and respond to potential cyber threats in real-time.
How are they different?
1. Level of autonomy
AI agents are like robot assistants who follow a strict set of rules. They only do what you tell them to and need updated instructions if you want them to do other things. Since AI agents don’t have their own opinions or thoughts, the judgments or decisions they make are based on predefined rules, algorithms, and patterns. For example, a chatbot can only respond to specific queries it was trained for, and need human customer service to answer questions outside that.
On the other hand, agentic AI systems are very autonomous and independent. They are able to make independent decisions, learn from their actions and environment. Their capabilities are much more advanced than AI agents. They can proactively identify and pursue strategic goals, evaluate options to make reasonable decisions, and even adapt in real-time. For instance, an autonomous vehicle is able to make driving decisions in real-time based on traffic and road conditions without needing human input.
Simply put, if AI agents are like someone who can only cook by following a certain recipe, agentic AI is like a chef who can create new dishes based on what’s available and always develops a more varied, delicious menu.
2. Task-oriented vs goal-oriented
AI agents are task-oriented. Their purpose is to perform specific functions such as sorting emails, setting reminders, handling customer service tasks, identifying potential influencers using data from their accounts, and detecting fraud. Agentic AI is more than that. They have more complex objectives and can adjust strategies depending on the situation to achieve those objectives. Agentic AI doesn’t live by rigid instructions, they focus on how to meet targeted results, operate independently, and decide everything they consider important.
3. Learning and decision-making capabilities
Both AI agents and agentic AI are excellent at performing their tasks, but they’re still different. While AI agents can accurately perform precise tasks by analyzing patterns, they lack independent learning and decision-making. They don’t adapt or evolve on their own unless the user updates or feeds them with more data or input. Their improvement is also only limited for updating information and accuracy, but they can’t change their decision-making process. Human interventions are still very dominant for AI agents.
Agentic AI has more advanced learning abilities, it can learn by doing - getting smarter as it works, without any human supervision. This learning doesn’t just help it work better, but also improves its decision-making process and ability to create new solutions, all without humans having to intervene.
How they function differently in influencer marketing
40.9% participants of Influencer Marketing Hub’s survey believe AI will revolutionize influencer marketing. Well, maybe we’re not too far from seeing that happen anytime soon. In fact, we’ve already seen some of this ‘revolution’ with the existence of AI-powered influencer marketing platforms. This is how AI agents and agentic AI can assist influencer marketing efforts:
AI agents in influencer marketing
AI agents in influencer marketing typically handle repetitive and laborious tasks like influencer discovery and outreach. To perform these tasks, they follow predefined rules or algorithms. This improves efficiency in influencer marketing efforts since AI agents excel at carrying out large-scale tasks like sorting and categorizing influencers.
For example, influencer marketing platforms like HypeAuditor helps brands identify suitable influencers by scanning profiles based on selected niche and filters. The platform then shows influencer analytics with different metrics, such as engagement rates, audience demographics, and following history. Once influencers are found and selected, the system can assist with influencer outreach or organize influencer lists. Further, these tools can track campaign performance, estimate ROI, and generate campaign reports.
Other than helping brands manage influencer recruitment and campaign management, AI agents can also use Natural Language Processing (NLP) which enables them to understand and interpret human language. In the influencer marketing industry, platforms like this are useful for conducting sentiment analysis, which digs deeper into how your brand is perceived by the public.
However, AI agents have limited adaptability, meaning they don’t adjust to changes quickly unless there's an update in their predefined programming. For instance, if the engagement of an influencer suddenly shifts due to a trend, an AI agent might not realize the change in time, and would heavily depend on human intervention, leading to outdated influencer choices.
Agentic AI in influencer marketing
On the other hand, agentic AI in influencer marketing acts as the refined version of AI agents. It has all the qualities of AI agents and can do even more. It’s no wonder that many people are excited about how agentic AI will transform influencer marketing in the future.
Not only can agentic AI help organize influencer lists and track influencer campaigns, but it can also make decisions autonomously. For example, if the AI notices an influencer is becoming popular in a new region, it might automatically reach out with a message like: “Hey [Influencer Name], we’ve seen an increase in engagement from your audience in [Region]. Let’s explore how we can expand your involvement in the campaign.”
Agentic AI can even take over some tasks that are normally done by human marketers, such as negotiating terms with influencers. If it has access to the right data (like influencer rates or preferred working conditions), it could handle the initial contract negotiations or adjust terms based on performance. This could include suggesting better payment terms or offering additional content ideas.
Due to its proactive nature, agentic AI would be able to also manage relationships with influencers by initiating conversations to check in content creation progress, provide feedback, or offer strategic suggestions as the campaign runs. This makes agentic AI the next level of influencer marketing campaign assistant.
The good and the bad of AI agents and agentic AI
Capgemini’s survey of 1,100 executives at large companies shows that 71% of respondents said AI agents will increase automation in their workflows, and 64% said they’ll improve customer service and satisfaction. This shows how AI is now perceived as something helpful and a considerable element for a company. More interestingly, 57% of them said the potential productivity improvements outweighed the risks. Is it so?
While we can harness the capabilities of AI agents and agentic AI, every technology comes with both benefits and risks. In this section, we’ll look at both so you can highlight what makes each valuable and where you might need to be cautious.
The upsides
Using AI agents in influencer marketing has proven to make work faster and more accurate. Additionally, it helps brands save more budget by reducing labor costs and increasing operational efficiency. Our latest research shows that marketers spend an average of 120 hours per campaign to manually manage influencer partnerships. Without AI, budget inefficiencies in influencer marketing can become a significant cost. For example, a campaign involving 50 micro-influencers would require an estimated $4.5K spent on marketers' salaries alone. These costs can be minimal if a company decides to use AI agents.
Another benefit is that both AI types can aid in decision-making. While AI agents and agentic AI perform decision-making differently, both are valuable. AI agents are great at performing decision-making in predefined tasks, such as determining if an Instagram influencer account is authentic or suspicious for fraud, or scoring brand/influencer sentiment. Agentic AI offers a more advanced benefit by providing hyper-personalized experiences. It will adjust influencer strategies in real-time based on the data it tracks to ensure your campaigns meet specific audience demands.
The downsides
Behind the bright spots, AI has its own risks and challenges, and these two AI types are no exception. One downside we’ve seen is how AI can never replace human creativity or detect subtle nuances in the influencer marketing process. No matter how advanced they become, AI cannot fully replicate emotions, feelings, or first-hand experiences like humans can.
Let alone feelings, AI agents rely heavily on predefined rules, meaning they struggle with unpredictable situations or tasks outside their original programming. To avoid missing opportunities, it’s best to combine AI agents’ work with human oversight for reality checks.
Although agentic AI brings more capabilities and automated workflows than AI agents, it also comes with bigger downsides. Firstly, this AI is very complex, making it expensive to develop and maintain, so small or medium-sized businesses might find the cost prohibitive. Secondly, since agentic AI makes decisions autonomously, it can sometimes take actions that are unpredictable or misaligned with human values. This would again require humans to intervene and correct if things go wrong. And since an agentic system can do (almost) everything on its own, there are ethical concerns about its implications. For example, agentic AI used for influencer selection might unintentionally favor influencers from certain backgrounds, leading to biased representation or excluding diverse voices.
On the other hand, both AI agents and agentic AI also pose high security and privacy risks since they handle sensitive information like influencer data and customer preferences. This presents a risk of data breaches or misuse. Improper data handling could lead to influencer profiles being compromised or personal data being exposed. There’s also concern about malicious code injections that could be hidden in documents read by AI, which the system then executes.
Unfortunately, the list doesn't end there. One of the biggest risks AI agents and agentic AI pose is their impact on human workers. Their ability to handle complex tasks and objectives makes it more challenging for humans, especially when it comes to cost comparisons. To minimize the impact of AI taking over human jobs, it’s best to place humans in higher-value tasks or take on roles as operators or quality checkers, so they can still monitor decisions and combine human and AI judgment.
Conclusion
It’s now clear that AI agents and agentic AI systems are different, especially in their capabilities and autonomy. While AI agents are more commonly used in real-world scenarios since they’re quite accessible for many brands, agentic AI remains exclusive and can even be considered something for the future in the influencer marketing industry. But with sophisticated and multitasking agentic AI like Manus entering the market, it’s important for brands to stay updated on these concepts, especially if they’re planning to jump on the AI wave soon - or sooner.
While it may seem like it’s a long way to go until we see an AI tool capable of making decisions and significantly reducing the human role in influencer marketing (even in areas like negotiating and giving feedback), it’s never too early to understand these technologies. With the rapid advancements in AI that we see almost every few months, the era we’re waiting for may arrive sooner than expected. When that time comes, make sure your brand is ready to stay ahead and know how to utilize all these AI technologies wisely.
