5 Levels Of AI Agents (Updated)
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7 min read
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Oct 11, 2024
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The phrases AI Agents, Autonomous Agents, Agentic Application, or what I refer to as Agentic X are all terms which are used interchangeably.
Some Background
I love the example from Agileβs rise in organisations, where project managers evolved into Scrum Masters, adapting to iterative development cycles.
Similarly, Conversational AI has transformed, shifting from basic chatbot frameworks to advanced prompt-engineering tools, and now into comprehensive AI Agent builders.
Understanding the components of an agentic framework is crucial in leveraging these advancements effectively.
In this article, I use the terms RPA, chaining, prompt chaining, and chatbot dialog flows interchangeably, as they all refer to a similar approach of using predefined, sequential nodes to guide processes.
What are Agents? In Shortβ¦
- Agentic applications leverage one or more Language Model as their core foundation or backbone, dynamically generating responses and actions.
- These applications manage states and transitions while constructing event chains in real-time to address specific user queries, providing adaptive solutions.
- Agents excel at handling ambiguous or implicit questions, breaking them into sequential sub-steps and iteratively processing through cycles of action, observation, and reflection until reaching a final resolution.
- Latency and cost management are critical for conversational implementations, balancing responsiveness with resource efficiency. Latency can be problematic with Agentic implementations.
- Inspectability and observability are essential for production implementations, with robust mechanisms developed to reveal the states and pathways traversed by agents, ensuring transparency.
- To accomplish tasks, agents have access to diverse tools, each equipped with a clear purpose β whether itβs making API calls, conducting calculations, or searching the web.
- Human-in-the-Loop (HITL) can serve as an auxiliary tool, enabling agents to seek human input when needed, expanding their operational capabilities.
- New agent tools can be seamlessly integrated to extend capabilities, allowing for continuous adaptation and enhancement of autonomous agent functions.
- Agents possess true autonomy, making decisions and performing actions independently, requiring minimal human supervision. Levels of autonomy are set by the number of iterations an AI Agent can cycle through, in order to reach a conclusion; and the number of tools at its disposal.
- With advanced flexibility, agents dynamically select and sequence tools based on situational needs, employing reasoning and adaptive strategies to solve complex tasks as they arise.
22 Key Differences Between AI Agents & Traditional Chaining/RPA
Below is an in-depth comparison of how AI Agents stand apart from traditional chaining and Robotic Process Automation (RPA) methods based on the criteria shownβ¦
1. Flexibility, Autonomy, Reasoning
AI Agents: Exhibit high flexibility and autonomy, capable of complex reasoning and decision-making based on context. They can adapt to unforeseen situations, respond to changing data, and make real-time adjustments.
Chaining/RPA: Typically follow predefined rules and sequences with limited flexibility. They execute tasks as they were programmed without deviation, making them less adaptable to changes in their environment.
2. Granular State-Based
AI Agents: Operate with a granular, state-based approach, maintaining an internal and dynamic state and understanding of the environment. This allows them to track changes over time and adjust their actions accordingly.
Chaining/RPA: Generally lack granular state awareness and operate on fixed workflows. They are less adept at handling complex or evolving tasks that require contextual awareness.
3. RPA Approach
AI Agents: Use machine learning and natural language processing to make decisions dynamically, going beyond rule-based automation.
Chaining/RPA: Rely heavily on traditional automation techniques, such as screen scraping and hardcoded rules, which are inflexible and require significant reconfiguration for any changes.
4.Human-in-the-Loop (HITL)
AI Agents: Often incorporate HITL for complex tasks or when encountering uncertain situations, allowing human intervention to guide the process or provide feedback.
Chaining/RPA: May involve humans for exception handling, but usually this is not a built-in feature. HITL is less integrated compared to AI agents. Tradition chatbots/chaining usually follow method of a complete transfer to an agent if the chatbot does not fulfil the intent.
5. Managing Cost
AI Agents: Can be costly to deploy and maintain due to resource requirements, though their adaptability and efficiency can lead to long-term cost savings.
Chaining/RPA: Often lower upfront costs, especially for simple repetitive tasks, but can become expensive if frequent updates and maintenance are needed.
6. Optimising Latency
AI Agents: Utilise optimisation strategies to minimise latency, often by prefetching data, parallel processing, or making real-time adjustments. But latency is often hard to improve for reasoning and decomposition tasks.
Chaining/RPA: May experience latency due to rigid workflows and sequential processing, with limited real-time optimisation. However, in general easier to optimise.
7. LLM-Generated Action Sequence
AI Agents: Use Language Models to generate action sequences dynamically, enabling them to handle complex, multi-step tasks based on evolving context.
Chaining/RPA: Action sequences are predefined and do not benefit from LLM-driven flexibility, limiting their ability to handle nuanced or conversational tasks.
8. Seamless Tool Integration
AI Agents: Often integrate various tools and services seamlessly, including APIs, databases, and external resources, to enhance functionality dynamically.
Chaining/RPA: Integration is typically more rigid, requiring manual configuration and less dynamic adaptation to new tools or services.
9. Explainability / Observability / Inspectability
AI Agents: Often include features for explainability and observability, providing insights into decision-making processes, which is critical for trust and compliance. But in most cases this aspect lacks.
Chaining/RPA: The sequence of events are set for different workflows.
10. Design Canvas Approach
AI Agents: Often rely on traditional programming environments with less focus on visual workflow design, making complex task configuration more challenging.
Chaining/RPA: May use design canvases for configuring complex workflows, often visually represented, allowing for intuitive adjustments and reconfiguration.
11. Conversational Oriented
AI Agents: Can engage in conversational tasks, using natural language understanding to interact with users effectively.
Chaining/RPA: Generally designed for conversational interfaces (considering traditional chatbot frameworks), though they can interact with simple text-based inputs.
12. Adaptive Learning Capabilities
AI Agents: can learn from new data and experiences over time, allowing them to improve autonomously, unlike traditional RPA which relies on pre-set rules and lacks learning capabilities.
13. Contextual Awareness
AI Agents have the ability to understand and adapt to the context of interactions, which enhances decision-making and response accuracy. RPA, chatbot flows, on the other hand, operates in a static, predefined context.
14. Dynamic Task Decomposition
AI Agents can break down complex tasks into smaller, manageable subtasks dynamically and adjust these based on real-time feedback. RPA/Chatbots/Prompt Chaining typically follows a linear, fixed sequence of tasks.
15. Real-Time Decision Making
AI Agents can make decisions on-the-fly based on live data, whereas RPA follows a preset decision-making process based on pre-programmed logic.
16. Unstructured Data Handling
AI Agents are capable of understanding and processing unstructured data, like natural language and images, through AI models, while RPA and workflows usually work with structured data which is well defined and classified.
17. Goal-Oriented Behaviour
AI Agents work towards high-level objectives and can modify their approach as needed, whereas Chaining scripts are designed to achieve specific tasks without overarching goals.
18. Scalability in Diverse Environments
AI Agents can be deployed in a wide range of environments and scale easily with minimal configuration changes, while RPA/workflows solutions may require significant customisation to adapt to different platforms or systems.
19. Proactive Engagement
AI Agents can initiate actions and engage proactively based on user behaviours or external triggers. RPA/workflows/chains are more reactive, executing tasks only when prompted by a specific event.
20. Tool Interoperability and API Flexibility
AI Agents are often designed to work seamlessly with a variety of tools and APIs, adapting as needed, whereas RPA/chains/workflows/chatbot solutions are generally more rigid and specific to certain tools or systems.
21. No Low-Code IDEs
AI Agents: development environments are typically more pro-code at this stage. Where RPA/Workflows/chaining/Chatbots are more established in their no-code design canvas approach to building.
22. Dynamic Adaptability to Unseen Scenarios
AI Agents: Can adapt to new and unseen scenarios by leveraging machine learning and contextual understanding, which allows them to make decisions even in cases they have not explicitly been trained for. This makes them highly adaptable to changing environments or unexpected user inputs.
Chaining/RPA: Typically struggle with scenarios outside their predefined scripts or rules. They operate based on specific sequences and can fail or require manual intervention when faced with unexpected situations or edge cases they were not designed to handle.
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Chief Evangelist @ Kore.ai | Iβm passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
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