Agentic AI is an advanced form of artificial intelligence (AI) that uses autonomous AI “agents” to carry out complex tasks without direct human supervision.
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Agentic AI Meaning
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 Generative 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 revolutionize entire industries ranging from finance, manufacturing, and healthcare to customer service, software programming, and cybersecurity.
Agentic AI vs AI Agents
Agentic AI is a software architecture for building AI that can plan, take actions, use tools, and learn from feedback with a bounded degree of autonomy. It covers the architectural patterns (e.g., planner–executor loops, tool-use, reflection), governance (human-in-the-loop, budgets, guardrails), and evaluation methods that make autonomous behavior reliable and safe. Use the term “Agentic AI” when you’re discussing strategy, architecture, or research patterns for autonomous behavior
AI agents are the actual software instances that apply those principles to real tasks. Think of a threat-intel enrichment agent or a phishing triage agent wired to specific APIs, data sources, and policies. Each agent runs with explicit limits (max steps, spend caps), logs every action, and is measured on task success, time-to-complete, and cost. Use the term “AI agent(s)” when you’re talking about a specific bot (or fleet) wired to tools and running in production.
How does agentic AI work?
According to our latest research on agentic AI systems, they 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 analyzes 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.
Benefits of Agentic AI in Business
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.
Agentic AI Risks and Challenges
As the use of agentic AI becomes more widespread, there are several key issues and challenges organizations 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 maximize 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, organizations 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.
Agentic AI Examples across Industries
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 analyze 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.
Agentic AI Cyberattacks
Organized cybercrime groups are now using agent-style automation to run parts of their operations. Here are the most common patterns:
- Phishing and social engineering: Attackers can use agentic workflows to generate tailored personalized lures, monitor response rates in real time, and adjust tactics on the fly. If an email bounces, the agent tries LinkedIn messaging. If MFA blocks access, it triggers MFA fatigue or kicks off voice phishing. If a domain is blocked, it auto-registers a look-alike and keeps going. The loop is continuous: measure → tweak → redeploy.
- Recon and targeting: Tasked agents crawl attack surfaces (domains, cloud assets, public repos) and map tech stacks. They flag weak points such as exposed credentials, stale S3 buckets, and misconfigured SaaS tenants, then prioritize them for human operators. This turns wide attack surfaces into ranked to-do lists.
- Rapid payload customization: Given an objective (e.g., data theft), agents assemble commodity components, mutate scripts to evade basic signatures, and repackage droppers/loaders to match the victim’s OS and controls.
- Chained intrusion tools: Agents chain scanners, password-guessing, and low-and-slow exfiltration. Data moves in small, timed bursts to stay beneath detection thresholds, with steps automatically throttled or reordered to avoid alerts.
- Business-model automation: For scams and financial fraud, agents run end-to-end playbooks: create and age accounts, post content, converse with victims, and escalate to a human only for money movement or other high-risk actions. This scales social-engineering operations with minimal operator time.
- Synthetic impersonation: Agents generate and A/B-test text, images, and voice clones to amplify disinformation, impersonate executives (“CEO fraud”), or pressure employees during active intrusions. The most convincing variants are promoted automatically.
- Resilience and evasion: When blocked, agents rotate infrastructure (proxies, domains), adjust tempo, and swap TTPs based on telemetry (“if delivery fails twice, change vector”). The constant adaptation complicates takedowns and attribution.
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.
According to Trend Micro's latest predictions on AI Cybersecurity, AI will also greatly impact the cybersecurity industry, quickly advancing the sophistiaction of cyber attacks
How can I get started with agentic AI?
Organizations that want to use agentic AI should begin by identifying their core goals and prioritizing 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 organization’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 defenses.
Fernando Cardoso
Vice President of Product Management
Fernando Cardoso is the Vice President of Product Management at Trend Micro, focusing on the ever-evolving world of AI and cloud. His career began as a Network and Sales Engineer, where he honed his skills in datacenters, cloud, DevOps, and cybersecurity—areas that continue to fuel his passion.
Frequently Asked Questions (FAQs)
What is agentic AI in simple words?
Agentic AI is a type of artificial intelligence that uses ‘AI agents’—mini AI programs—to perform tasks and make decisions without human intervention.
What is the difference between agentic and non-agentic AI?
Agentic AI can use tools or AI agents to perform more complicated tasks in semi-autonomous ways whereas non-agentic AI do not.
What is the difference between GenAI and agentic AI?
In GenAI (generative AI), a single model responds to user prompts to answer questions or create content. In agentic AI, autonomous "agents" within the system carry out tasks without user prompting to accomplish a goal.
What is the difference between AIOps and agentic AI?
Agentic AI can be used for almost any task. AIOps focuses exclusively on using AI to improve the efficiency of IT operations.
Is ChatGPT an agentic AI?
No. ChatGPT is an example of Generative AI (GenAI), not agentic AI.
Is a chatbot agentic AI?
Some chatbots use agentic AI, but most do not. In general, agentic AI is much more advanced and autonomous than a chatbot.
Who are the agentic AI leaders?
Many companies use or develop AI agents. Some of the leaders in the field include Microsoft, Google, OpenAI, Adept AI, and Anthropic.
What is an example of an agentic AI?
An example of agentic AI is a self-driving car, which monitors its environment, carries out tasks, and makes complex decisions in real time.
What is the best agentic AI?
There is no one “best” agentic AI, but some of the leading agentic AI platforms are Microsoft’s AutoGen, Relevance AI, Cognosys, UiPath, and CrewAI.
Where is agentic AI being used?
Agentic AI is being used in a wide range of industries, from healthcare and finance to customer service, HR, marketing, and cybersecurity.