Agentic AI: The breakthrough push for the high -tech future?

As we all know, The most advanced AI chatbots today use generative AI (generative AI) to give feedback based on every single interaction. In there, the user asks a question using a command form and the chatbot will use natural language processing (NLP) to give feedback.
With the constant development of technology, A new generation of artificial intelligence has been born, Agentic AI, The AI ​​generation has sophisticated reasoning capabilities and detailed planning to automatically solve complex problems. This technology promises to improve productivity and operations in many different industries, creating a new playing field for businesses in the digital era.

What is Agentic AI??

Agentic AI is essentially artificial intelligence systems with certain "autonomy" capabilities, can act autonomously to achieve specific goals set by humans.

Different from traditional AI models - which only respond to requests or perform predetermined tasks - Agentic AI can make decisions, make a plan of action and constantly learn from its own experiences. This allows them to proactively solve complex problems instead of just following instructions.

One highlight that helps Agentic AI go beyond traditional AI is its ability to "chain". This means they can perform a series of consecutive actions to complete a single request, by breaking down complex tasks into more specific and manageable steps.

What makes Agentic AI outstanding is its ability to be proactive. Don't just follow orders, it also predicts demand, come up with creative solutions and adapt to real-life situations. Agentic AI not only solves problems but also optimizes the approach for maximum efficiency.

Imagine a digital assistant that not only facilitates work but also understands, Predict needs and deliver solutions you never thought of – that's the power of Agentic AI.

How Agentic AI Works?

Agentic AI operates through processes 4 steps to solve the problem:

1. Awareness (Perceive): Agentic AI collects and processes data from different sources such as sensors, databases or digital interfaces. This process includes the extraction of salient features, object recognition or identification of related entities in the environment.
2. Argument (Reason): A large language model (LLM) acts as a coordinator or reasoning engine, helps understand the task, provide solutions and coordinate with specialized models for specific functions (create content, image processing or recommendation system). This process uses techniques such as Retrieval Enhanced Generation (Retrieval Augmented Generation – RAG) to access exclusive data sources and provide accurate results, Fit.
3. Hành động (Act): By integrating with external tools and software via API, Agentic AI can quickly perform tasks based on the proposed plan. Protection mechanisms (guardrails) can be integrated into Agentic AI to ensure they perform tasks accurately. For example, An Agentic AI supporting customer care can handle claims up to a certain limit, For requests that exceed limits, human approval will be required.
4. Học hỏi (Learn): Agentic AI continuously improves through a feedback loop, also known as “data rotation” (data flywheel), where the data generated from its interactions will be fed back into the system to improve the model. Adaptability and “active learning” give businesses a powerful tool to drive better decision making and improve operational efficiency.

Agentic AI learns like humans ?

Agentic AI became a reality thanks to computers increasingly excelling at image recognition and language understanding. These advances, mainly driven by advanced technologies based on the Transformer model.

GenAI models are trained on huge volumes of data including text, image, sound, videos and figures. Now, they can handle a wide range of tasks such as summarizing information, pandemic, answer the question, image editing, create sound, convert voice to text. However, These models are not truly “autonomous” – they need specific commands or instructions to produce the most accurate results.

This is when Agentic AI - a new step in the field of artificial intelligence - appears. Similar to humans taking on specific job roles and tasks, Agentic AI builds independent teams of AI agents. These agents work closely together, Use reasoning and planning to solve problems step by step, with large language models (LLM) Acts as the "brain" to make decisions.

Agentic AI is designed to work like humans: handle tasks independently, group work, Self-evaluate progress and improve through each iteration cycle.

While AI creates (Generative AI) dependent on human instructions and unable to handle complex problems or multi-step reasoning on its own, Agentic AI excels thanks to its network of learning agents, adapt and coordinate flexibly, make your own decisions and continuously improve performance – similar to how humans naturally work and progress.

Application of Agentic AI in business

Agentic AI's applications are extremely diverse, is only dependent and limited by human creativity and skill. From simple tasks like creating and distributing content, to more complex applications such as enterprise software management, Agentic AI is gradually changing the landscape of many fields in the market.

• Customer service: Agentic AI improves customer service by enhancing self-service and automating everyday communications. Theo Salesforce, More than half of the customer service professionals surveyed noted a significant improvement in customer interactions., helps reduce response time and increase satisfaction. Besides, “virtual characters” (digital humans) – AI agents are designed with a shape and personality that reflects the brand's identity. These “characters” provide realistic interactions, in real time, Help the sales department take care of customers or solve problems directly via phone during peak hours.
• Content creation: Agentic AI can quickly create quality marketing content with high personalization, helps save on average 3 hours per content (theo Hubspot). Thanks to that, Businesses can focus on building strategies and innovating plans, helps stay competitive and improve customer relationships.
• Software engineering development: Agentic AI helps automate repetitive programming tasks, Helps programmers increase labor productivity. Predicted up to five 2030, AI can automate to 30% programmers' working time.
• Medical: For doctors who have to analyze large amounts of medical data and patient information, Agentic AI can filter important information, Help doctors make accurate decisions. Besides, Agentic AI can automate administrative tasks and provide support 24/7, Helps doctors spend more time on medical care and professional research.

Challenges and risks of Agentic AI

Besides the benefits, Agentic AI also faces many challenges, risk

• Labor market disruption: Agentic AI can "replace" job positions such as data entry, administrative procedures, investment process, asset management or auditing. This change requires people to cultivate more professional knowledge as well as new AI skills in the current digital age.
• Privacy and cybersecurity: Agentic AI's reliance on huge amounts of data raises privacy concerns. Balancing personalization and privacy is important, That requires legal corridors to be controlled in parallel with taking advantage of resources from this new AI factor..
• Market fluctuations: Agentic AI can reduce the barriers to automated interactions in the market, This could lead to increased systemic risks and increased market volatility. Automated decisions from AI can cause herd mentality (herding behavior), leading to sudden market fluctuations, unpredictable and difficult to control.
• Governance and regulations: Legal frameworks need to be regularly updated by the Government with the continuous development of AI to ensure correctness, effective supervision as well as necessary ethical standards, to address biases (bias) in the decision-making process (for example, in credit approval). Transparency is very important to maintain user trust and protect them from unnecessary risks..
• Interpretation ability (Explainability): Stakeholders need a clear view of AI decision-making, especially in high-risk fields such as finance, insurance, bank. The European Union's data protection law – GPDR provides a framework for the responsible implementation of AI., emphasizes the importance of defining responsibilities and obligations in explaining AI decisions.
• Fiscal policy and cooperation: International Monetary Fund (IMF) proposes to apply an “automation tax” to support the adaptation process of the workforce. Besides, There is a need to establish cooperation between financial institutions and regulators to ensure a secure financial future, sustainable.