Agentic AI is an advanced form of artificial intelligence (AI) that uses autonomous AI “agents” to carry out complex tasks without direct human supervision.
Table of Contents
In-depth definition of agentic AI
Artificial intelligence (AI) uses computer systems that can perform tasks typically requiring human intelligence, such as learning, reasoning, problem-solving, and pattern recognition.
Agentic AI is a form of AI that uses machine learning (ML) algorithms, large language models (LLMs), natural language processing (NLP), and other advanced technologies to create “AI agents”—autonomous, self-directed AI systems that can accomplish complex tasks by using tools that allow them to interface with external systems without needing constant human input or direction.
Agentic AI versus traditional AI
While traditional AI models can generally only follow prompts or other instructions given to them by a human user, agentic AI can act autonomously. This enables it to make decisions, solve difficult problems, work with other AI agents, carry out tasks, and learn from past interactions without being prompted and with little to no human oversight or supervision.
As the technology continues to evolve and become more widespread, agentic AI has the potential to revolutionise entire industries ranging from finance, manufacturing, and healthcare to customer service, software programming, and cybersecurity.
How does agentic AI work?
Agentic AI systems usually include these five aspects:
1. Percieving—the agentic AI uses sensors, open and proprietary databases, application programming interfaces (APIs), and other sources of information to gather vast amounts of data and learn about its environment.
2. Reasoning—next, it analyses the data to identify patterns, understand what it’s being asked to do, and plan the best course of action.
3. Making decisions—based on those patterns, the agentic AI uses algorithms to make decisions, predict possible outcomes, and create strategies to achieve its goals.
4. Taking action—the system takes a series of actions to implement its strategies and carry out tasks, such as generating text or responding to a customer inquiry.
5. Learning from experience—lastly, the agentic AI can be made to evaluate how well it achieved its goals to improve its efficiency and accuracy in the future.
What are the benefits of agentic AI?
Agentic AI offers a number of benefits over traditional AI. These include:
- Can work autonomously to select the tools required to carrying out tasks
- Increased efficiency and speed by connecting multiple process workflows.
- Proactively chooses the appropriate tool to use to accomplish the task.
- Less human supervision required – improves productivity.
- Maintains context and state over time
- Probabilistic, novel solutions instead of fixed responses.
What are the risks and challenges of using agentic AI?
As the use of agentic AI becomes more widespread, there are several key issues and challenges organisations must keep in mind.
For example, as with all LLMs, the data used to build agentic AI models can include gaps, inaccuracies, or biases that could influence how the AI reacts or limit its effectiveness. There is also a need to safeguard private, sensitive, and confidential information when developing or using AI to ensure compliance with all laws and regulations, including the General Data Protection Regulation (GDPR).
AI agents with too few safeguards can go overboard carrying out their tasks, with unintended consequences. For example, a stock-trading AI agent might use and suggest risky or illegal practices to maximise profits for clients. Some AI agents can also repeat or self-reinforce errors in how they reason, plan, or interact with customers.
To address these challenges, organisations must make sure their use of agentic AI is open, ethical, and transparent and connected to appropriate tools. They need to give instructions that are clear, careful, and include as much context as possible. And they should make sure their AI security and AI cybersecurity measures are robust, proactive, and up to date.
Examples of real-world applications of agentic AI
Agentic AI is already being used in a variety of industries, including:
- Healthcare—AI agents monitor patient data and provide recommendations for different diagnoses and treatment options.
- Financial services—agentic AI bots analyse stock prices, provide recommendations to human traders, and carry out trades measured in fractions of a second.
- Customer service—agentic AI chatbots respond to inquiries, resolve client complaints, and create a smoother and more efficient customer experience.
- Self-driving vehicles—are agentic AI systems that use GPS, sensors, and real-time data to detect hazards, monitor traffic, and plan safe, efficient routes.
- Supply chain management—AI agents automate supply chain and manufacturing processes, track inventories, and manage suppliers.
- Cybersecurity—AI agents continuously scan for vulnerabilities, automate threat detection and response, and defend against cyberattacks in real time.
What is the future of agentic AI?
As agentic AI continues to become more intelligent, adaptive, and autonomous, it will almost certainly become an even more ubiquitous part of our daily lives than it is today.
Innovations in the ability of agentic AI to reason, learn, and integrate seamlessly with other technologies will doubtless speed up its adoption across a broader range of industries and make individuals and businesses more efficient and productive.
Agentic AI could also lead to a profound shift in the workforce, taking on routine tasks previously handled by human beings while humans adopt new roles that require greater creativity, critical thinking skills, and human-machine collaboration.
How can I get started with agentic AI?
Organisations that want to use agentic AI should begin by identifying their core goals and prioritising investment in AI applications that can help them achieve their objectives as efficiently as possible.
They should look for agentic AI solutions that can adapt, evolve, and scale to take advantage of the latest tools to accomplish typical tasks. They should also offer ongoing support and training to help their employees work more effectively with AI agents and take full advantage of all the benefits agentic AI has to offer.
Where can I get help with agentic AI for cybersecurity?
The Trend Vision One™ AI Security solution safeguards your AI stack and strengthen your enterprise security posture using the industry's first proactive cybersecurity AI (including agentic AI features) that removes blind spots and addresses vulnerabilities before attacks occur.
Incorporating the full AI capabilities of Trend Cybertron—the world's first truly proactive cybersecurity AI—Trend Vision One can transform an organisation’s security posture from reactive to proactive, improve the speed and accuracy of their threat detection and response, and dramatically enhance the efficiency and effectiveness of their cybersecurity defences.