Major companies like NVIDIA and IBM, which specialize in semiconductors and AI-related products, are turning their attention to the next AI innovation, Agentic AI, which continues to evolve.
Recently, French AI startup “H,” which raised $220 million in May 2024, has launched “Runner H,” a platform for developing agentic AI applications. It simplifies integrating natural language processing and machine learning, enabling developers to deploy AI solutions quickly. “Runner H” aims to help companies automate processes, boost efficiency, and strengthen competitiveness.
Agentic AI represents an advanced form of AI systems that go beyond traditional approaches by possessing “autonomy,” “goal-orientation,” and “decision-making capabilities.” Unlike previous AI models, these systems don’t merely process information or execute tasks, but can independently plan and execute actions, finding optimal methods to achieve given goals.
Characteristics of Agentic AI
1. Autonomy
- Capable of operating without human intervention
- Observes environments and independently determines actions toward goal achievement
- Example: Self-driving cars making real-time decisions about optimal routes based on traffic conditions
2. Goal-Driven Behavior
- More than just a programmed system, flexibly acting to achieve predefined goals
- Independently plans and executes necessary tasks for goal attainment
3. Situational Awareness and Adaptability
- Collects environmental data and adapts actions accordingly
- Example: Robots navigating around obstacles to reach destination points
4. Learning Capability
- Improves performance based on past experiences and data
- Example: Conversational AI enhancing responses according to user preferences
5. Decision-Making Skills
- Selects optimal actions based on available information
- Example: Financial AI analyzing market trends to propose optimal investment strategies
Source;NVIDIA https://blogs.nvidia.com/blog/what-is-agentic-ai/
How does Agentic AI work?
1. Goal-Oriented Design
Agentic AI is developed with clearly defined objectives or goals, and it operates to achieve them efficiently. For example, optimizing product prices in e-commerce or guiding a robot to navigate obstacles and reach a destination.
- Example: An AI system in e-commerce analyzes inventory and market demand to dynamically adjust pricing for maximum profitability.
2. Environmental Awareness
Agentic AI gathers information about its surroundings using sensors or external data sources and analyzes it in real time.
- Example: A self-driving car uses cameras and LiDAR sensors to monitor road conditions and the movement of other vehicles.
3. Autonomous Decision-Making
Based on the collected data, Agentic AI selects appropriate actions. This process is guided by pre-programmed rules or machine learning models.
- Example: A chatbot understands a customer’s query and generates a suitable response.
4. Feedback Loop
Agentic AI evaluates the outcomes of its actions and uses the results to refine its decision-making and behavior. Through this loop, it learns from experience and adapts.
- Example: A game AI analyzes a player’s actions and adjusts its strategy accordingly for the next interaction.
Source;NVIDIA https://blogs.nvidia.com/blog/what-is-agentic-ai/
Rather than relying on humans to provide detailed instructions for specific tasks, it will independently carry out processes such as identifying and analyzing problems, executing solutions, and learning—adopting an approach closer to how humans operate
Applications of AgenticAI
AgenticAI is designed to transform industries through its wide range of applications. Some of the key areas where it excels include:
- Customer Support Automation:
Streamline customer interactions with intelligent chatbots and virtual assistants capable of understanding and responding to complex queries. - Personalized Marketing:
Deliver tailored content and recommendations to customers, increasing engagement and conversion rates. - Healthcare Advancements:
AI agents help doctors analyze complex medical data, streamline administrative tasks, and capture clinical notes, freeing up time to focus on patient care and stronger doctor-patient relationships. - Software Engineering
AI agents are enhancing developer efficiency by handling repetitive coding tasks. By 2030, it’s estimated that AI could automate up to 30% of working hours, allowing developers to concentrate on more intricate problems and foster innovation (NVIDIA).
Wrap
Agentic AI represents a new generation of AI with advanced autonomy and adaptability, driving innovation across business, healthcare, education, and numerous other domains. While promising, addressing its challenges remains crucial for responsible development and implementation.
The era of a one-way relationship where humans simply control AI is coming to an end. Soon, Agentic AIs will collaborate with each other and with humans as equals, working together on business and everyday tasks. That future is not far away.
As more sophisticated AgenticAI emerges, the relationship between humans and AI will continue to evolve, prompting us to rethink how we engage with it.
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