Lately, the word "agentic" has been everywhere. In just the last six months, searches for the term have jumped by over 640%. So, what’s driving all this interest?
It all comes down to the rise of agentic AI — a smarter form of artificial intelligence (AI) that doesn’t just wait for commands but can reason and take action on its own.
But the concept of being agentic isn’t new. Before its use in AI, it’s been used for years in psychology and education to describe people who take control of their learning and decision-making.
So, what exactly does it mean to be agentic, and why should you care? Let’s break it down.
What does agentic mean?
Agentic means being “capable of achieving outcomes independently.” In other words, it’s used to describe someone or something who has agency — and the power to act.
Think of an AI assistant that doesn’t just provide responses but takes action on its own. For example, an AI assistant in IT support that automatically detects system issues, resolves minor bugs without intervention, and escalates major problems to engineers — without waiting for a ticket to be filed.
That’s an example of agentic AI in action.
Today, you’ll most often see “agentic” used as part of the phrase “agentic AI,” but the word has long existed before in educational and psychological contexts to describe human learning and functioning, albeit slightly differently.
What does agentic mean in education?
In education, agentic learning (also known as agentic engagement) is a type of learning where the student actively contributes to their own instruction. In other words, they are encouraged to “be agentic” and proactively take ownership of their own learning.
For example, imagine a student in a coding class who not only completes assigned exercises but also builds their own app on the side. They’re demonstrating agentic learning by going beyond the curriculum and taking control of their education.
What does agentic mean in psychology?
In a psychological context, the meaning of the word agentic is slightly different. Here, it refers to psychologist Stanley Milgram’s agentic state theory, which describes how people sometimes transfer responsibility for their actions to an authority figure, essentially surrendering their agency.
A classic example is Milgram’s obedience experiment, where participants followed instructions to administer electric shocks to another person, believing they weren’t personally responsible for their actions.
This demonstrated how individuals could enter an agentic state and defer responsibility to a perceived authority.
What does agentic mean in business AI?
Agentic AI refers to AI systems that can autonomously pursue complex goals, make decisions, and plan, adapt, and execute multi-step processes — all without explicit human supervision or intervention.
Essentially, agentic AI can act almost like a human employee. Not only is it capable of understanding context and instructions in natural language, but it has enough intelligence to reason through subtasks and adapt its decisions and actions based on changing conditions.
Imagine an AI-powered HR assistant that not only schedules interviews but also evaluates resumes and identifies top candidates. That’s agentic AI in action.
In the context of business artificial intelligence, agentic frameworks greatly enhance basic AI models, enabling them to autonomously set and pursue their own goals.
They can also adapt to new information and carry out complex tasks, such as troubleshooting technical issues or automating entire workflows. In some cases, agentic AI systems can even perform physical tasks in the real world through robotic devices.
Agentic AI vs. machine learning: What’s the difference?
Agentic AI shares some similarities with machine learning (ML), as both kinds of AI can independently improve with experience.
ML helps computers learn from data and get better over time without being explicitly programmed. It uses algorithms and math-based models to recognize patterns to improve their performance from experiences.
For example, an ML algorithm can use customer behavioral datasets to predict which customers are most likely to churn.
So what’s the difference? Machine learning alone is limited to making predictions or recommendations — it might “learn,” but it doesn’t act on its own.
Agentic AI, on the other hand, is a more advanced form of AI that’s capable of:
- Autonomous, goal-directed action and decision-making
- Proactive problem-solving
- Complex reasoning and planning
- Autonomous learning and adapting
Agentic AI goes beyond the simple pattern recognition of traditional ML algorithms to engage and interact with its environment and carry out complex, multi-step, specific tasks without little to no human oversight.
A great real-world example is an AI-driven employee help desk that not only provides answers but also predicts common employee issues, proactively resolves problems, and continuously improves itself based on past interactions.
However, while agentic AI may be more sophisticated than traditional ML alone, it’s worth noting that machine learning and agentic AI are often used together. In fact, machine learning is often a core component of agentic AI systems. Machine learning algorithms can be used to train the agentic AI system to reason and make decisions.
Learn more about how agentic AI works.
How agentic AI works
Agentic AI can reason, plan, take action, and learn to adjust its behavior over time. But it has to process large amounts of data to identify patterns and relationships and power autonomous decision-making.
This is where large language models (LLMs), natural language processing (NLP), and ML come in, empowering agentic AI to understand data, generate insights, and take independent action.
- LLMs enable agentic AI to understand and create human-like text.
- NLP processes and analyzes raw language inputs to better understand user queries and intent.
- ML allows AI to learn from data, adapt its behavior, and continuously self-improve over time.
Key characteristics of agentic AI
What makes agentic AI stand out from traditional AI models? Here are some of its defining characteristics:
- Autonomous actions
Agentic AI can make independent decisions and take action without explicit human intervention. This means it can exhibit independence and adaptability in dynamic environments.
- Goal-setting
Unlike basic ML algorithms that only identify patterns in data and predict outcomes, agentic AI can set and pursue specific goals. So it’s much more proactive and strategic than simple ML.
- Adaptive learning
Agentic AI can learn from experiences and adapt to changing environments. It can even use reasoning abilities to choose the best course of action, self-optimizing its behavior in response to real-time feedback and changing conditions to stay on track toward its goal.
- Reasoning and planning
- Traditional ML executes repetitive tasks based on pre-learned patterns and static algorithms. But agentic AI uses autonomous learning, reasoning, and real-time environmental feedback to make decisions and adapt its strategies as needed. So it can handle complex tasks that require multiple steps and decisions.
- Proactive problem-solving
Unlike traditional ML algorithms, agentic AI can independently identify problems, judge which solution can best serve its goals, then proactively take action to problem-solve and address issues before they escalate.
Agentic AI vs. AI agents: What’s the difference?
You might be wondering — how is agentic AI different from AI agents?
AI agents are individual entities designed to perceive their environment, reason, and act autonomously. They can range from simple, rules-based systems to more complex, deep-learning models.
For example, AI agents can include chatbots, recommendation systems, and even self-driving cars.
In a business setting, AI agents generally fall into two categories:
- Productivity AI agents focus on helping employees with everyday tasks, like summarizing documents or retrieving information from knowledge databases.
- Business operational AI agents focus on bigger or more complex tasks like streamlining HR workflows or assisting IT teams by proactively updating tickets.
But it’s important to distinguish between AI agents and agentic AI.
While AI agents operate individually, agentic AI is a system where multiple AI agents work together to accomplish more complex tasks.
For example, a customer service chatbot on its own is just an AI agent that can answer basic customer queries. But when it’s incorporated into a complete agentic AI system, that chatbot can proactively identify customer needs, suggest relevant solutions, and even escalate complex cases to a human agent when necessary.
How agentic AI is transforming business automation
More businesses are turning to automation to optimize workflows, support employees, and improve productivity.
Normally, developers use integration platforms as a service (iPaaS) solutions to connect applications, integrations, and streamline operations.
These solutions make it easier to sync data across different systems — without requiring extensive custom coding.
But here’s how agentic AI takes things to the next level.
Unlike traditional automation, which follows rigid, predefined rules or iPaas solutions which need significant amounts of manual coding and configuration, agentic AI can adapt and learn over time. It’s more flexible, making it ideal for handling unexpected scenarios or changing environments and for rapidly building automations.
Here’s how agentic AI enhances traditional iPaaS solutions to level up business automation:
- Autonomous self-improvement: Agentic AI continuously refines its own behavior based on new data and experiences, making workflows smarter over time.
- Greater versatility: Its adaptability enables more intelligent and dynamic data integration across platforms to support a variety of business needs.
- Enhanced usability: Finally, unlike traditional iPaaS solutions, agentic AI leverages LLMs, NLP, and ML to interact more naturally with users and other systems. This powers better contextual understanding and responsiveness, making it both easier and more pleasant to work with.
Agentic AI may be a new term, but businesses investing in intelligent automation are already seeing results. Two-thirds of companies have implemented some kind of more basic task-based automation — but the organizations that incorporated intelligent automation achieved an average cost reduction of 32%.
Explore how agentic AI is driving the next evolution of enterprise AI.
Agentic AI streamlines automations and accelerates enterprise workflows
Agentic AI is still relatively new, but it’s already making big advancements in business automation. One survey found that by 2028, 33% of enterprise software applications will incorporate agentic AI, up from less than 1% in 2024.
It’s no surprise that agentic AI is becoming a go-to solution for intelligent automation. Its benefits can go far beyond efficiency — it can also help you to quickly and easily use or build automations. This lets you streamline workflows across every department, from HR onboarding to IT troubleshooting and processing invoices in finance.
Moveworks is leading the way in agentic AI, revolutionizing how businesses like yours automate workflows. With Moveworks’s agentic AI Assistant, you can:
- Get instant answers across systems, so your employees can quickly find what they need — without having to wait for human help.
- Automate routine tasks like password resets and software access, saving your team time and effort.
- Seamlessly integrate with tools like Slack, Microsoft Teams, and ServiceNow to keep everything running smoothly.
With Moveworks, your team can get work done faster, easier, and with less manual effort.
Ready to see how agentic AI can transform your business? Just check a couple of boxes and we’ll send over your own customized AI agent roadmap.
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