A Simplified Guide to Agentic AI

The robots are coming! And they planned their trip without any human oversight.

Agentic AI is the latest evolution of AI, and it goes far beyond the capabilities of its cousins, traditional and generative AI.

So, what is agentic AI, and what’s all the hype about? Read on for a simple explanation, examples, and some of the challenges agentic AI presents.

A simple explanation of agentic AI

Agentic AI is a proactive and autonomous type of artificial intelligence. Without human oversight, it continuously learns, reasons, plans, and acts until it completes a goal.

Agentic AI is more flexible and adaptable than earlier AI because the technology can:

• Operate independently of humans
• Understand its environment (business and physical)
• Make sense of context and nuance
• Break down big objectives into small tasks
• Focus on goal-oriented behavior

While agentic AI may be a new concept, it’s already making an impact:

• By 2029, agentic AI will resolve 80% of common customer service issues without human intervention, according to Gartner. This is expected to result in a 30% reduction in operational costs.
• Another Gartner prediction is that 33% of enterprise software will incorporate agentic AI by 2028. That’s a substantial increase from less than 1% in 2024.

How is agentic AI different from traditional AI?

There’s a time and a place for traditional AI. But it has limited flexibility, making it difficult to go beyond simple tasks. Plus, it’s reactive. It requires humans to create prompts and tell the system what to do.

Agentic AI is more advanced, with sophisticated reasoning and planning abilities. It can start tasks without human instruction and work toward bigger, more complex goals than traditional AI.

Here’s an example:

Think of a traditional task-specific AI assistant, like a customer service chatbot. The chatbot is reactive and rules-based, meaning it depends on humans to create a fixed set of instructions and prompts. You might use the chatbot to check an order status or check the amount of a bill.

Taking this a step further, a chatbot in an agentic AI system can handle customer queries, complaints, payments, and returns across all digital channels. Agentic AI allows the chatbot to resolve specific issues quickly and escalate others to human representatives.

How is agentic AI different from generative AI?

The biggest difference between generative AI and agentic AI is that one creates new content (generative) and the other acts to achieve a specific goal (agentic).

Generative AI

Generative AI is reactive and more dependent on humans than agentic AI. Before a generative AI system can do anything, a human has to give it a prompt, like generating images or creating product descriptions.

You can produce text, code, images, video, and audio. But generative AI is constrained by the prompts it receives, the instructions defined by humans, and the training data it was built on. So, generative AI can only generate responses based on what it’s been told or shown to understand.

Agentic AI

Agentic AI is proactive. It can start working toward a goal without any human involvement.

For example, a self-driving car doesn’t need a human to avoid obstacles on the road. It’s equipped with sensors and advanced algorithms that provide context so the system can take action and adapt.

3 benefits of agentic AI

1. Cost and resource savings

Although there is a high upfront investment, agentic AI brings long-term savings in many ways.

• Cuts labor costs: Agentic AI automates tasks that usually require extensive human work.
• Operates 24/7: No need for a human babysitter; agentic AI runs autonomously in the background.
• Produces more accurate results: Agentic AI handles repetitive tasks that are often subject to human error, and continuously learns from experiences to enhance its preciseness.

There are also industry-specific benefits. For example, agentic AI helps manufacturers cut costs and minimize waste by optimizing stock levels and predicting demand fluctuations.

2. Faster and better decision-making

An agentic AI system uses sophisticated algorithms to analyze data, make predictions, and adapt to change. It understands the context and nuance of its environment and is always learning from new data and tasks.

Decision-making becomes easier because real-time data informs outcomes. The more agentic AI works, the faster and more precise it gets, and the better decisions it helps you make.

3. Scalability without growing pains

Growing a business means more customers and higher profits. But everything else grows too, like the amount of data you have to store, manage, and analyze.

Scaling is much easier with agentic AI because you don’t have to worry about the typical growing pains. You can create infinite AI agents to handle the increased workload or to focus on new tasks, without increasing your headcount. And since agentic AI automates many tedious and repetitive tasks, decision-making is faster and operations are more efficient.

Agentic AI vs. AI agents

What’s the difference between agentic AI and AI agents? The terms are often used interchangeably, but they are different concepts.

• An AI agent is an individual application built to work on specific tasks, such as filtering spam in an email inbox.
• Agentic AI is a broader concept. It is a category of AI that focuses on developing autonomous AI models so that AI agents can operate independently from humans.

Think of an AI agent as a GPS navigation system. A human needs to tell the GPS tool where they want to go, and the GPS responds with driving directions.

On the other hand, agentic AI can be compared to a self-driving vehicle. An agentic AI system can perceive its surroundings, like other cars and buildings, and it makes decisions on how to adapt to unexpected situations, like traffic or accidents.

Within the agentic AI system, multiple agents collaborate to solve complex problems. The AI agent in charge of GPS is just one of many in an agentic AI network for a self-driving car.

What are multi-agent systems (MAS)?

Multi-agent systems are a component of agentic AI. Each agent in a system works autonomously, and they collaborate, communicate, and coordinate actions to accomplish goals.

An Agile Scrum team structure is a real-life example of a multi-agent system. The project management methodology involves several roles, including a product owner, developers, business representatives, and other specialists.

Each team member has an area of expertise. Together, they work toward a common goal, like building and launching an application.

How does agentic AI work?

For agentic AI to work autonomously, it must:

• Understand and interact with its environment
• Process information from various sources
• Make decisions
• Plan the appropriate tasks to achieve its goal

Five components of agentic AI make this happen:

1. Perception

The first component of agentic AI perceives the business or physical environment and processes information from databases, tools, and sensors (microphones, cameras, etc.).

For example: Let’s say you’re planning a multi-stop business trip from New York to London and Paris and want to know if it will rain.

Since agentic AI is more advanced and understands nuance, you can ask the AI agent a vague question, like “Do I need to pack an umbrella for my trip?”

The agent will work to understand the context and then identify the tools it needs to access to provide an answer.

2. Reasoning

Agentic reasoning (or cognition) works similarly to the human problem-solving process. It involves processing information, analyzing options, and making autonomous decisions.

Reasoning works off of perception and memory to:

• Process information gathered in the perception component
• Autonomously translate knowledge into action
• Analyze data and evaluate options

For your business trip, the reasoning component breaks down the problem into small tasks. These include accessing your trip details, work calendar, and weather forecasts.

Then it develops a plan that the action component executes.

3. Action

With plans in place, the action component can start:

• Translating plans and goals into actions
• Integrating with external systems and data via APIs
• Creating and executing workflows
• Deciding which of the available tools it should use to complete the goal

The action component needs to compare your trip details and work schedule to the weather forecast for each city.

It can access your work calendar to see when your out-of-office is set and then log into your British Airways account to check when you have flights booked. From there, it compares the information to reports from weather.com.

The AI will then tell you to pack a travel umbrella since there’s a 90% chance it will rain in London while you’re there.

4. Memory

Agentic AI systems learn and store knowledge and experiences in their memory to improve performance and decision making.

Memory in Agentic AI covers everything from remembering your login credentials for a website to recalling past experiences.

If you were worried about rain the last time you went to London, Agentic AI’s memory will apply this knowledge the next time you plan to visit the city. Then it can proactively let you know whether or not to bring that umbrella.

5. Learning

The learning component is what separates early AI from agentic AI.

Through a feedback loop known as a data flywheel, agentic AI continuously learns from new data and experiences. The memory is updated with the latest knowledge so the AI agent can better evolve and adapt to changing circumstances and feedback. Performance improves over time as the system produces more accurate results.

Examples of agentic AI

From aerospace to healthcare to retail, agentic AI is used in nearly every industry worldwide. Here are a few agentic AI use cases to give you an idea of what the technology can do.

Logistics and supply chains

Goal: Route optimization
For busy supply chains and vendors, manual delivery routing is time-consuming, costly, and inefficient. What if there’s a long traffic delay? Or a sudden downpour of rain that makes driving unsafe?

Agentic AI takes care of all the “What ifs” by:

• Planning and updating delivery routes in real time
• Monitor traffic, weather, and delivery windows
• Adjusting to disruptions or changes in demand

Supply chains use agentic AI to reduce fuel and labor costs and improve on-time delivery, making both the business and the customer happy.

Manufacturing

Goal: Autonomous robotics and factory automation
Traditional automation is far from flexible. Manual intervention is always required when introducing new products or process changes.

Guided by agentic AI, intelligent robots on the shop floor can adapt to changing production demands, adjust assembly lines, and manage bottlenecks.

AI agents are proactive and dynamically reprogram robots to optimize operations by reassigning tasks and updating workflows, all without human oversight.

Agentic AI in manufacturing works throughout a product lifecycle, from planning to production. It increases throughput, supports rapid scaling, and enhances a manufacturer’s agility.

Fintech

Goal: Expense management automation
Manually tracking expenses is tedious work and prone to human error.

While traditional AI can automate some of this work, agentic AI takes it further by tracking, categorizing, and analyzing your expenses. The system can provide actionable insights into your spending patterns and offer personalized savings tips to improve your financial health.

Challenges of agentic AI

Like any new technology, there are a few things to be cautious of before bringing agentic AI into your business.

Financial investment

The initial cost of AI is often a deterrent for most businesses. While the technology promises savings in the long term, the upfront investment is a considerable undertaking.

If you don’t have in-house AI experts, you’ll need to invest time and money into training, resources, and setting up the infrastructure. Enterprise AI systems can be especially complex, and it’s not uncommon to burn through budgets just in the development phase.

You’ll also need to forecast the operational expenses of agentic AI. How many AI agents are running? What are the energy and storage costs? What is the price per prompt? Do the math upfront to avoid unexpected expenses.

Skills gap

In many ways, AI is evolving faster than we can keep up with.

First, there was predictive AI, then generative AI, and now agentic AI. Every few months, a new type of AI technology promises to be better than the last, and businesses struggle to address the skills gap.

While 75% of companies are adopting some kind of AI, only 35% of workers have received AI training. And that’s just for traditional AI. Many organizations are years away from implementing agentic AI strategies because of the skills gap.

Ethics and accountability

Agentic AI creates ethical concerns because you’re giving a machine the power to analyze data and take action without checking with a human first.

Beyond the typical AI hallucinations and errors, how the AI is trained and used makes all the difference.

Here’s a cautionary tale. Amazon used an AI tool to aid in recruiting, but soon found that it favored men over women. The tool automatically downgraded resumes that mentioned “women’s” or included graduates of women’s colleges.

Why did this happen? Because the AI tool was trained on hiring data that favored men.

This also raises another concern: Accountability. Who is responsible for these ethical issues? The business owner, the technology, or the programmer? These are questions every business should answer before implementing autonomous technology.

Security risks

It’s a business’s responsibility to protect sensitive user data, which is why another deterrent to AI is security.

AI works with massive amounts of private information across many different systems that, if exposed, can cause a world of problems.

One concern is prompt injection, a security attack against large language models (LLMs). Hackers try to trick an AI model into leaking sensitive data or spreading misinformation by writing malicious prompts that can override developer instructions. This is one of many examples of the possible security risks of agentic AI.

Get started with agentic AI

Ready to get started with agentic AI? Low-code platforms are the ideal way to do it. Not only are they user-friendly, but some also offer starter applications to help you build your first AI agents.

To start building, try Mendix for free.