AI Mini Series: AI Agents: Compound Systems and Agentic Approaches Titelbild

AI Mini Series: AI Agents: Compound Systems and Agentic Approaches

AI Mini Series: AI Agents: Compound Systems and Agentic Approaches

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 Briefing Document: AI Agents Introduction: This document reviews two sources discussing AI agents. The first source, "Understanding AI Agents," provides a foundational understanding of what constitutes an AI agent, its structure, and different types. The second source, "What are AI Agents?" delves into the practical application of AI agents, highlighting their increasing importance within compound AI systems and contrasting agentic approaches with more traditional programmed systems. Together, these sources offer a comprehensive overview of AI agents, their capabilities, and their future. Key Themes and Ideas: Definition and Core Concepts: AI Agent Defined: An AI agent is an autonomous software entity that interacts with its environment, perceives, reasons, and acts to achieve specific goals. They operate via a cycle of sensing, thinking, and acting.Key Characteristics:Autonomy: Agents operate without direct human intervention.Perception: They gather information from the environment through sensors or data inputs.Action: Agents act upon the environment to achieve their objectives.Goal-Oriented Behavior: They are designed to achieve predefined goals. Structure of an AI Agent: Perception Subsystem: Processes raw data from the environment and transforms it into meaningful information.Decision-Making Engine: Uses reasoning algorithms (rule-based systems, optimization algorithms, machine learning) to determine the best action.Actuator Subsystem: Executes chosen actions to influence the environment.Learning Module (Optional): Enables the agent to learn from past experiences. Types of AI Agents: Simple Reflex Agents: Follow condition-action rules (if-then logic) without internal state. (Example: A thermostat)Model-Based Agents: Use an internal model of the environment to predict outcomes. (Example: Navigation apps)Goal-Based Agents: Take actions that lead to specific goals. (Example: Chess-playing AI)Utility-Based Agents: Optimize actions based on a utility function to quantify the desirability of outcomes. (Example: E-commerce recommendation systems)Learning Agents: Continuously improve performance by learning from past experiences. (Example: Robotic vacuum) Practical Applications of AI Agents: Healthcare: Virtual health assistants, medical image analysis.Finance: Automated trading, fraud detection.Autonomous Vehicles: Self-driving navigation.Customer Service: Chatbots.Gaming: Dynamic and adaptive AI opponents. The Shift from Monolithic Models to Compound AI Systems: Monolithic Models Limitations: Limited by training data, hard to adapt and can give incorrect answers when they don't have access to the appropriate informationCompound AI Systems: Solve problems by building systems around models and integrating them into existing processes with multiple components. Allows for more modular approaches.Example of Compound System: The example given of planning a vacation is that the system would query a database to determine vacation availability, then return that information using an LLM.Benefits of System Design: Allows for breaking down complex tasks, picking the right components (tuned models, large language models, image generation models, programmatic components). Quicker to adapt and easier than tuning a model.RAG as Example: Retrieval Augmented Generation is highlighted as a common example of a compound AI system.Importance of Control Logic: The path to answer a query which is often programmed by the human designing the system. LLM Agents: Shifting Control Logic: Agentic Approach: Puts the large language model in charge of the logic. Leveraging improved reasoning capabilities to develop a plan to tackle a problem and iterate.Thinking Slow vs. Thinking Fast: Shifts system design away from fast, programmed actions towards slower, plan-driven approaches.Capabilities of LLM Agents:Reasoning: LLM at the core of problem-solving, develops a plan.Acting: Uses external programs ("tools") to execute plans. Examples include search, databases, calculators, APIs.Memory: Stores inner logs and conversation history for context and personalization. ReACT Framework: Combines reasoning and acting capabilities. The agent takes a prompt, plans, acts using tools, observes the output, and iterates on the plan as needed. AI Autonomy Spectrum: A sliding scale of autonomy where the trade-offs are considered based on the complexity and narrowness of the tasks.For narrow problems, the programmatic approach can be more efficient than the generic agent route.Agentic approaches are useful for complex tasks with a spectrum of possible queries, where it would be difficult to configure every path in the system. Ethical Considerations: Autonomy vs. Control: Determining the appropriate level of agent autonomy and safeguards against harm.Bias in Decision-Making: Ensuring fair and unbiased decisions in sensitive areas.Transparency: Designing agents that can explain their decisions.Accountability: Establishing who is responsible for agent ...
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