In the wave of rapid development in artificial intelligence, we have witnessed the rise of generative AI and the widespread adoption of large-scale multimodal models (LMM). However, with the advancement of technology and the expansion of application scenarios, Andrew Ng proposed Agentic AI in his speech at BULIT 2024, which is becoming a new focus in the AI field. This technology not only drives innovation in the design concept of agents but also provides new pathways for efficiently solving complex tasks. At the same time, he mentioned the potential of visual AI in handling unstructured data, which may usher in a new era of AI applications.
This article will delve into five aspects of this technological transformation: the core concepts of Agentic AI, its technical advantages, design patterns, application scenarios, and trends in visual AI.
1. What is Agentic AI?
Agentic AI is an emerging agent technology characterized by higher autonomy and the ability to solve complex tasks. Traditional AI relies more on static task inputs and simple reasoning patterns, while Agentic AI introduces more advanced workflows, including:
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Task Planning: Decomposing tasks and formulating step-by-step solutions.
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Reasoning Ability: Dynamically optimizing problem-solving methods under various conditions.
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Multi-agent Collaboration: Division of labor and collaboration among different AI roles to jointly complete complex tasks.
The core of this technology lies in enabling AI to think and execute tasks from multiple perspectives, similar to human capabilities, through efficient processes and modular design.
2. How Does Agentic AI Change AI Development?
1.From Concept to Implementation: A Leap in Efficiency
Traditional AI system development often takes months or even longer, including data labeling, model training, and deployment. The emergence of generative AI has shortened this process to a few days, allowing teams to validate ideas more quickly.
With the support of Agentic AI, developers can build agent prototypes in a more flexible manner. For example, scenarios that previously relied on supervised learning now only require a well-designed prompt to enable AI to complete tasks in a short time. This efficiency not only saves development time but also reduces trial-and-error costs.
2.The Development Philosophy of “Move Fast and Be Responsible”
Andrew Ng emphasized the principle of “Move fast and be responsible” in his speech. Compared to the traditional motto of “Move fast and break things,” Agentic AI advocates for promoting technological development in a responsible manner. Through prototype design, assessment, and testing, development teams can ensure product stability and reliability while innovating.
3.Enhanced Multimodal Processing Capability
The multimodal capabilities of Agentic AI are particularly exciting. It can not only process text but also interpret images and videos through visual AI technology. For example, agents can first extract key information from an image and then combine it with text analysis for reasoning. This multi-step task processing approach enables AI to achieve improvements in both quality and accuracy.
3. Four Core Design Patterns of Agentic AI
The powerful capabilities of Agentic AI are attributed to its four design patterns, which play important roles in the development process.
1.Reasoning and Planning
Reasoning is one of the core capabilities of Agentic AI. When handling complex tasks, AI can formulate solutions through multi-step logical deductions. For example, when given the task of “generating an image of a girl reading a book,” Agentic AI will first plan the actions and design the scene, then use various tools to complete the drawing and rendering. This planning ability allows AI to perform complex tasks more calmly.
2.Reflection Design Pattern
Reflection is a design method to enhance output quality. AI self-evaluates its generated content, identifies issues, and iteratively improves. For instance, a code generation task can allow AI to check and optimize its initial code, ultimately producing higher-quality results. This iterative design pattern enables AI to excel in precision.
3.Function Calls Design
In the function calling mode, Agentic AI can autonomously decide when to call external tools to complete specific tasks. For example, it can trigger web searches, run code, or process data. This design greatly expands the task boundaries of AI, giving it higher flexibility and adaptability.
4.Multi-agent Collaboration
Multi-agent collaboration is a method for decomposing complex tasks. By allowing different agents to take on roles such as planning, execution, and feedback, the system can achieve a highly optimized task completion process. For example, in a medical scenario, one agent is responsible for data collection, while another provides diagnostic suggestions, ultimately forming a comprehensive solution. This model draws on the concept of parallel computing and offers significant advantages in practical applications.
Read more about the four strategies of agents
Product Manager Li Yujun, WeChat Official Account: Product Manager Li Yujun. Andrew Ng reveals: The four strategies of AI agents enhance the performance of large language models.
4. Application Scenarios of Agentic AI
1.Legal Field
In legal document processing, Agentic AI can quickly analyze contract terms and generate compliance recommendations. This capability reduces the time required for manual review and minimizes the risk of human error.
2.Medical Diagnosis
By combining multimodal data, Agentic AI can analyze patient medical records and imaging data to provide accurate diagnoses and treatment plans. For example, one agent can focus on image analysis while another extracts information from textual medical records, ultimately generating a comprehensive diagnostic report.
3.Corporate Compliance Management
In highly complex regulatory environments, Agentic AI can automatically generate compliance reports through multi-step reasoning and adjust recommendations based on the latest regulations, saving companies significant costs.
5. Visual AI: The Next Major Trend in AI Development
1.Value Extraction from Unstructured Data
Andrew Ng specifically mentioned that visual AI will be at the core of the next technological revolution. The vast amounts of image and video data stored by enterprises are often underutilized, while visual AI can transform this unstructured data into business value. For example, logistics companies can use visual AI to analyze the flow of goods in surveillance videos, optimizing supply chain management.
2.From Zero-shot Reasoning to Multi-step Processing
Similar to text generation, visual AI is evolving from simple zero-shot reasoning to more complex multi-step task solutions. By first detecting key areas in images and then analyzing them step by step, visual AI can complete tasks more intelligently.
3.Emerging Application Scenarios
Visual AI shows tremendous potential in fields such as retail, security, and healthcare. For example, retailers can analyze consumer shopping behavior using visual AI to improve service quality; in security, intelligent identification of abnormal behavior can enhance public safety.
6. The Future of Agentic AI and Visual AI
Andrew Ng’s speech indicates that the combination of Agentic AI and visual AI will further broaden the application boundaries of AI. In the future technological ecosystem, these two technologies may become key competitive factors for enterprises.
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Technology Integration: Combining the reasoning capabilities of Agentic AI with the data processing capabilities of visual AI can create more powerful intelligent systems.
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Developer Tools: By providing standardized design patterns and APIs, the barriers to using Agentic AI and visual AI can be lowered, benefiting more enterprises.
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Emerging Industries: In fields such as education, creative design, and social governance, Agentic AI and visual AI will lead new application trends.
The rise of Agentic AI and visual AI marks the transition of artificial intelligence from single-task processing to multi-task collaboration. This transformation not only drives technological innovation but also brings unprecedented opportunities for developers and enterprises. Against the backdrop of rapid development in generative AI, Agentic AI is redefining the application boundaries of AI through its planning, reasoning, and collaboration capabilities. Meanwhile, the potential of visual AI in extracting value from unstructured data suggests that a new revolution is on the horizon.
In the future, as technology matures and scenarios expand, Agentic AI and visual AI will become the core engines driving social development. This is undoubtedly an era for innovators, worthy of our collective anticipation and exploration.
References:
https://www.youtube.com/watch?v=KrRD7r7y7NY
https://mp.weixin.qq.com/s/ACjn-r2Kxzos3RMJiNxhEQ
Content generated with AI assistance
Images from the internet & AI generated