AI Agent and Agentic AI are currently popular applications of AI, ranging from various tools to enterprise application systems, all filled with expectations of transforming through the use of AI Agent or Agentic AI.
So, what exactly is an AI Agent? What is Agentic AI? What are their differences and connections?
First, let’s look at their basic concepts:
- AI Agent
is an intelligent entity capable of autonomous understanding, perception, planning, memory, and tool usage, typically working within a relatively limited range of requirements, with the goal of efficiently and accurately completing specified tasks.
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Classic architecture of AI Agent
- Agentic AI
refers to intelligent systems with high autonomy, adaptability, and proactivity, capable of setting tasks, formulating plans, flexibly adapting to environments, and actively learning and optimizing their behavior.
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Architecture of Agentic AI
Next, let’s compare in detail from several aspects: technical foundation, core characteristics, application scenarios, ethics and risks, applicable scenarios, and development potential:
1. Technical Foundation
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Supervised learning, reinforcement learning, rule engines, etc.
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Reinforcement learning, meta-learning, large models combined with self-supervised learning
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Limited perception and understanding, mainly handling specific input signals
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Multimodal perception ability, understanding complex environments and various data inputs
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Based on fixed rules or algorithms optimizing specific tasks
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Dynamic decision-making, goal generation, adaptive behavior planning
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Limited to specific domains, related to tasks
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Wide-ranging, capable of transferring and learning across various tasks
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2. Core Characteristics
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Passive execution, relying on user or environmental triggers
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Active goal setting, autonomous action optimization
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Clear task boundaries, environmental changes may lead to failure
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Highly flexible, capable of adapting to complex environments and dynamic changes
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Single or fixed range tasks, such as customer service, navigation
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Wide-ranging multitasking capabilities, such as automated planning of complex systems
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Limited, usually requiring manual intervention for training
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Can continuously improve through self-supervised learning and online learning
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Low to medium, executing specific tasks does not require complex strategies
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High, requires coordination of multiple objectives and balancing short- and long-term benefits
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Mostly user-driven interactions, such as Q&A or control commands
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Higher intelligent interaction capability, can predict needs and interact proactively
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3. Application Scenarios
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Smart home control, recommendation systems, voice assistants
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Personalized life management systems (e.g., comprehensive health optimization assistants)
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Case screening, diagnostic assistance, simple monitoring
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Comprehensive health management (actively discovering health risks and providing solutions)
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Automated trading, risk assessment
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Proactively optimizing investment portfolios, long-term wealth planning
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Customer service robots, process automation
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Strategic planning AI, intelligent decision support systems
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Data processing tools, modeling assistants
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Autonomously discovering research directions, automatically generating research hypotheses
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Intelligent tutoring (answering questions)
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Customized learning path design, comprehensive learning planning
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4. Ethics and Risks
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High, developers and users have complete control over its behavior
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Lower, high autonomy may lead to unpredictable behavior
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Clear, responsibility lies with developers and users
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Complex, may involve consequences of system decisions and uncontrollable behaviors
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Range of data collection and usage is relatively controllable
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Higher data dependency, may lead to privacy and ethical issues
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Lower risk, usually operates in controlled environments
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Needs to guard against potentially misleading objectives and overly optimized behaviors
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Lower, mainly initiated by users
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Higher, may be designed as systems with potentially malicious or misleading objectives
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5. Applicable Scenarios and Development Potential
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Suitable for specific tasks with clear requirements
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Suitable for complex, multi-task, and dynamically changing scenarios
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Current Technology Status
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Widely deployed, such as chatbots, recommendation systems
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Still developing, some show initial characteristics
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Enhancing efficiency, optimizing specific tasks
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May drive AI towards general artificial intelligence (AGI)
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Improving human efficiency, freeing up labor
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Potentially disruptive impact, requiring careful control and guidance
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Related Reading:
NVIDIA: Agentic AI Solves Complex Problems Autonomously in Four Steps
