Agentic AI

In the rapidly evolving field of artificial intelligence (AI) and machine learning , new technologies are always appearing. An innovative use of artificial intelligence is agentic AI, which operates more autonomously than other kinds of AI.

Also known as autonomous AI or AI agents, agentic AI refers to AI systems designed to operate independently, making decisions and taking actions without direct human intervention. These systems can perceive their environment and reason, learn from experience and execute tasks to achieve specific goals .

What is agentic AI?

Agentic AI definition: Agentic AI refers to artificial intelligence systems that possess agency, enabling them to function autonomously and make independent decisions without the need for continuous human oversight.

Unlike traditional AI, which often requires direct human control or supervision, agentic AI can direct its own actions and adapt to dynamic conditions, thereby performing tasks and achieving objectives on its own. However, achieving full autonomy without any human supervision remains a goal for the future.

A key advancement in this field is the development of multimodal agents . An agent is a software entity that can perceive its environment, make decisions and take actions to achieve specific goals. These agents integrate various types of data, such as text, audio, images and video, to offer a deeper and more accurate understanding of their environment and user interactions.

By processing and interpreting multiple data modalities, multimodal agents enhance AI’s robustness and versatility , allowing it to more closely mimic human autonomy and decision-making. As these agents learn and adapt, they improve efficiency, quality and operational excellence, augmenting human capabilities. In essence, agentic AI enables computer systems to mimic human behavior and thinking. The more these agents learn and adapt, the more closely they can mimic human autonomy and decision-making.

How does agentic AI work?

Agentic AI has the following key defining characteristics that differentiate it from other kinds of AI and define how it works:

• Autonomy: Agentic AI systems operate independently, making decisions and executing tasks without direct human intervention. These capabilities allow them to handle complex scenarios and respond to real-time changes without waiting for human input.
• Adaptability: Agentic AI systems can adjust their actions based on new data and changing conditions, allowing them to function effectively in dynamic environments. They continuously learn from their experiences and improve their performance over time.
• Focus on goals: Agentic AI is designed to achieve specific objectives, often optimizing its behavior to meet these goals efficiently. It prioritizes tasks and allocates resources to ensure the best possible outcomes.
• Memory: The system retains context and uses past experiences to inform future decisions, enhancing its ability to perform complex tasks. This contextual awareness helps in making more accurate and relevant decisions.
• Planning: Agentic AI can break tasks down into manageable subtasks and create step-by-step plans to achieve its goals. It can anticipate potential obstacles and adjust its plans accordingly to successfully complete these tasks.
• Integration: These systems can use external tools and data sources to enhance their capabilities, moving from data generation to active task execution. This integration allows them to leverage a wide range of resources to improve their effectiveness.
• Multiagent collaboration: Agentic AI can work alongside other AI systems, sharing insights and optimizing task division to improve overall performance. This collaborative approach enables more efficient problem-solving and innovation.

What is the history of agentic AI?

Throughout the last century, agentic AI has evolved significantly, integrating advancements from various AI subfields to create autonomous systems capable of independent decision-making and action.

Here are some key milestones in the history of agentic AI:

• 1956, the Dartmouth Conference: The term “artificial intelligence” was coined at this conference, marking the beginning of AI as a field of study. Early AI research laid the groundwork for future developments in autonomous systems.
• 1980s, expert systems: During the 1980s, expert systems became popular. These systems used rule-based algorithms to mimic the decision-making abilities of human experts, a precursor to more advanced autonomous agents.
• 1990s, intelligent agents: The concept of intelligent agents gained traction in the 1990s. These agents were designed to perform specific tasks autonomously, such as web crawling and automated trading.
• 2000s, reinforcement learning: The 2000s saw significant advancements in reinforcement learning, a key component of agentic AI. This technique allows agents to learn from their environment through trial and error, optimizing their actions to achieve specific goals.
• 2010s, deep learning and neural networks: The integration of deep learning and neural networks in the 2010s enabled more sophisticated pattern recognition and decision-making capabilities in autonomous agents. This period also saw the beginning of self-driving cars and advanced robotics.
• 2020s, multiagent systems: The 2020s have been marked by the development of multiagent systems, where multiple autonomous agents work together to complete complex tasks. This era has also seen the integration of large language models (LLMs) into agentic AI, enhancing these models’ ability to understand and generate human language.
• Mid-2020s, mainstream adoption: Agentic AI has become a mainstream technology, with applications in various industries, including finance, healthcare and manufacturing. These systems can now handle complex, multistep activities autonomously, driving significant advancements in efficiency and innovation.

Agentic AI continues to evolve, integrating new technologies and methodologies to enhance its capabilities. As we look to the future, the potential applications of agentic AI are vast, promising to revolutionize how we interact with technology and automate complex processes.

How is agentic AI evolving?

Agentic AI represents the culmination of advancements in various AI components and subfields, evolving to enable sophisticated autonomous systems. This evolution can be broken down into several key phases:

• Integration of machine learning: The integration of machine learning (ML) allowed agents to learn from large datasets, improving their ability to make decisions and perform tasks. This was a significant step forward from rule-based systems, as agents could now adapt to new information and improve over time.
• Advances in natural language processing (NLP): Natural language processing enabled agents to understand and generate human language more effectively, making interactions more natural and intuitive.
• Development of reinforcement learning: Reinforcement learning techniques allowed agents to learn from their environment through trial and error, optimizing their actions to achieve specific goals.
• Multimodal capabilities: The ability to process and integrate information from various sources, such as text, images and audio, enhanced the effectiveness and versatility of AI agents.
• Autonomous decision-making: Combining these advancements, agentic AI systems can now make autonomous decisions, plan actions and execute tasks without human intervention.

How is agentic AI used?

The transformative impact of agentic AI in customer support and finance is deeply rooted in its autonomous characteristics and decision-making capabilities. In customer support, AI-driven chatbots and virtual assistants use natural language processing and machine learning to understand and respond to customer inquiries independently.

These systems can make decisions on the fly, such as escalating complex issues to human agents or providing tailored responses based on the customer’s history and preferences, thereby ensuring efficient and personalized service.

In finance, agentic AI systems use advanced algorithms to autonomously detect fraudulent activities. To do so, they analyze transaction patterns and identify anomalies in real time. Additionally, in autonomous trading, these AI systems make split-second decisions to buy or sell assets based on market data, trends and predictive analytics.

This level of autonomous decision-making not only enhances security and accuracy but also optimizes financial performance by executing trades at the most opportune moments. These capabilities underscore the power of agentic AI to autonomously navigate complex environments and make informed decisions that drive business success.

At Micron, agentic AI plays a pivotal role in our smart manufacturing, using the autonomous decision-making capabilities to enhance every step of the silicon manufacturing process. Given the complexity of this process, which involves around 1,500 steps over several months, agentic AI systems are integrated with smart sensors to monitor quality and collect real-time data.

During the wafer fabrication stage, agentic AI detects minute quality deviations and autonomously adjusts parameters to maintain optimal conditions, enhancing output and yield. The AI systems analyze detailed images and videos, identifying microscopic flaws in real-time across the entire process, from wafer creation to assembly and testing.

Additionally, agentic AI is used for preventative maintenance and quality control, with acoustic sensors and thermal imaging detecting machinery issues early. These AI-driven systems prevent costly shutdowns, enhance worker safety and contribute to sustainability by reducing waste and energy consumption. This application of agentic drives improvements in efficiency, quality and operational excellence.

All of these AI systems autonomously analyze the data, making real-time adjustments to optimize operations, improve accuracy and boost productivity. Ultimately, our use of these systems significantly enhances yield, quality and efficiency.