2024 is poised to be the year of AI agents. To understand AI agents, we need to examine the significant shifts in the field of generative AI. One noteworthy shift is the transition from monolithic models to compound AI systems.
Models alone have limitations due to the data they are trained on, which affects the tasks they can solve and their adaptability. For instance, tuning a model requires significant data and resources.
Suppose I want to plan a vacation and determine how many days of vacation I have left. Inputting this query into a standard model would yield an incorrect answer, as the model lacks access to my personal data. However, a compound AI system can integrate the model into existing processes, like accessing a database containing my vacation information.
To address this problem:
This compound AI system demonstrates that certain problems are better solved using principles of system design, involving multiple modular components like tuned models, image generation models, output verifiers, and searchable databases.
AI agents control the logic of compound AI systems by leveraging advancements in large language models' reasoning capabilities. These agents can reason, act using external tools, and access memory.
Large language models can break down complex problems and create plans, adjusting as needed to achieve success.
Agents determine when and how to call external tools like web search, calculators, code, or translation models to optimize their problem-solving approach.
Agents store inner dialogues and conversation history to enhance personalization and improve the interaction experience.
By combining reasoning and acting components, REACT agents can:
For a complex task like determining how many 2 oz sunscreen bottles to bring to Florida, an agent would:
As compound AI systems become more agentic, system designers can choose between programmatic routes for narrow, well-defined problems and agentic approaches for complex, diverse tasks. This balance can optimize efficiency and adaptability.
AI agents represent a significant advancement in AI, with the potential to handle increasingly complex tasks autonomously. As we continue to explore these technologies, their integration into everyday processes will become more seamless and efficient.
AI agents are sophisticated AI systems that leverage reasoning, acting, and memory capabilities to autonomously solve complex tasks.
Unlike traditional monolithic models that rely heavily on pre-trained data, AI agents use multiple modular components and external tools to solve problems, making them more adaptable and capable.
A compound AI system integrates various AI models and programmatic components, allowing it to perform more complex and personalized tasks compared to monolithic models.
AI agents can reason (create and adjust plans), act (use external tools for problem-solving), and access memory (store and retrieve inner dialogues and conversation history).
The REACT approach combines reasoning and acting components. It prompts the AI to plan its response and use external tools, iterating until it provides a final answer.
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