Written by Ken Huang, CEO of DistributedApps.ai and Co-Chair of AI Safety Working Groups at CSA.

There is no clear and consensus definition of what an AI agent is in the literature. This article does not aim to define what an AI agent is. Rather, I focus on examining AI agents from a range of capabilities, spanning from basic data processing to complex autonomous decision-making. This framework allows us to explore the progression of AI agents through different levels of sophistication, highlighting the core features, functionalities, and distinctions at each stage.

Level 1: Perception and Data Processing

At the foundational level, the focus is on an AI agent's ability to process sensory data (images, text, audio, etc.) and extract features for computation. Metrics include recognition accuracy, precision, recall, and efficiency. Multi-agent considerations are absent as this stage is purely about individual data-processing capabilities.

Level 2: Reasoning and Problem-Solving

The emphasis here is on logical reasoning, inference, and structured problem-solving for individual agents. The focus is on tasks like executing algorithms, finding solutions within constraints, and optimizing outcomes in well-defined environments. Multi-agent systems are not directly relevant unless the task explicitly requires inter-agent interaction.

Level 3: Learning and Adaptation

This level involves the ability of individual agents to improve performance over time through supervised, unsupervised, or reinforcement learning. While individual learning remains the priority, collaborative learning or competitive adaptation might appear in specific scenarios (e.g., game simulations or shared environments), but these are exceptions rather than core features.

Level 4: Context Awareness

Agents must understand and adapt to their environment, including spatial, temporal, and social dimensions. Multi-agent considerations begin to emerge here in scenarios where agents share a dynamic environment, such as in robotics or autonomous navigation systems, requiring mutual awareness but not full collaboration.

Level 5: Autonomy and Decision-Making

At this stage, an agent demonstrates autonomy by making decisions independently in dynamic environments. Multi-agent considerations appear in decentralized systems, where individual decisions may impact or rely on other agents. For example, distributed systems like supply chain management may require agents to autonomously coordinate actions without centralized control.

Level 6: Collaboration and Coordination (Multi-Agent Introduced)

This is the first level where multi-agent systems become a primary focus. Agents collaborate to achieve shared objectives, requiring mechanisms for communication, task allocation, and conflict resolution. This level evaluates how well agents work collectively, balancing individual and group goals. Metrics include team efficiency, robustness to agent failure, and quality of inter-agent cooperation.

Level 7: Communication and Interaction

Agents are evaluated for their ability to communicate effectively, interpret intent, and maintain contextual relevance. Multi-agent considerations are significant when agents must share information, negotiate, or resolve conflicts, as in swarm intelligence or distributed planning systems.

Level 8: Creativity and Innovation

Creativity involves producing novel and valuable outputs, such as designing new solutions or strategies. In multi-agent systems, creativity might involve emergent behaviors where collaboration leads to innovative results. However, this level can also be assessed for individual agents, so multi-agent dynamics are context-dependent.

Level 9: Ethical and Value Alignment

Agents are evaluated for their ability to align actions with ethical principles and societal norms. In multi-agent systems, this involves ensuring collective behaviors adhere to ethical constraints, such as fairness, bias mitigation, and privacy preservation.

Level 10: General Intelligence

This level corresponds to Artificial General Intelligence (AGI), where agents can perform tasks across diverse domains with human-like flexibility. Multi-agent considerations apply when general intelligence emerges in systems of agents working collaboratively, but an individual AGI agent can also operate independently.

Level 11: Self-Improvement and Meta-Learning

At this ultimate level, an agent demonstrates the ability to improve its own architecture, learning strategies, and operational methods. Multi-agent systems may play a role if agents collaboratively refine their algorithms, but the focus is on individual and systemic self-improvement.

Let us use a diagram to summarize these 11 levels:

About the Author

Ken Huang is a prolific author and renowned expert in AI and Web3, with numerous published books spanning AI and Web3 business and technical guides and cutting-edge research. As Co-Chair of AI Safety Working Groups at the Cloud Security Alliance, and Co-Chair of the AI STR Working Group at the World Digital Technology Academy under the UN Framework, he's at the forefront of shaping AI governance and security standards.

Huang also serves as CEO and Chief AI Officer (CAIO) of DistributedApps.ai, specializing in Generative AI-related training and consulting. His expertise is further showcased in his role as a core contributor to OWASP's Top 10 Risks for LLM Applications and his active involvement in the NIST Generative AI Public Working Group.

His publications include:

1. Beyond AI: ChatGPT, Web3, and the Business Landscape of Tomorrow (Chief Editor) - Strategic insights on AI and Web3's business impact
2. Generative AI Security: Theories and Practices (Springer) - A comprehensive guide on securing generative AI systems
3. Practical Guide for AI Engineers (Volumes 1 and 2) - Essential resources for AI practitioners
4. The Handbook for Chief AI Officers: Leading the AI Revolution in Business
5. Web3: Blockchain, the New Economy, and the Self-Sovereign Internet (Cambridge University Press) - Examining the convergence of AI, blockchain, IoT, and emerging technologies