Google's White Paper on Agentic Systems

Google recently released a white paper titled "agents" where they share how to effectively build agentic systems.

Introduction to Agentic Systems

Introduction to Agentic Systems In this video, we will look at key insights from this paper, which is especially useful if you're building systems on top of agents.

Defining AI Agents

People use different definitions for agents, but now there seems to be a convergence or agreement that agents are going to be a key part of building systems on top of AI. Defining AI Agents The white paper by Google provides a comprehensive overview of how to effectively build agentic systems.

Components of an AI Agent

An AI agent is made up of several components, including reasoning frameworks, tools, and data stores. Components of an AI Agent Understanding these components is crucial in building effective agentic systems.

Differences Between Agents and Models

Agents and models are two different concepts in AI, and understanding their differences is essential in building systems on top of AI. Differences Between Agents and Models The white paper provides a clear explanation of these differences and how to apply them in building agentic systems.

Reasoning Frameworks for Agents

Reasoning frameworks, such as React, Chain of Thought, and Tree of Thought, are essential in building agentic systems. Reasoning Frameworks for Agents These frameworks provide a structured approach to building agentic systems.

Tools for Enhancing Agent Capabilities

Tools such as extensions, functions, and data stores are essential in enhancing the capabilities of agents. Tools for Enhancing Agent Capabilities These tools provide a wide range of functionalities that can be used to build complex agentic systems.

Enhancing Model Performance with Targeted Learning

Targeted learning is a technique used to enhance the performance of models, and it can be applied to agentic systems as well. Enhancing Model Performance with Targeted Learning This technique involves using a small set of examples to fine-tune the model and improve its performance.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique used to expand the knowledge of AI models, and it can be applied to agentic systems as well. Retrieval-Augmented Generation (RAG) This technique involves using a combination of retrieval and generation to produce more accurate and informative responses.

Conclusion

In conclusion, building agentic systems is a complex task that requires a deep understanding of the components of AI agents, the differences between agents and models, and the reasoning frameworks and tools used to build these systems. By applying the techniques and tools discussed in this video, developers can build more effective agentic systems that can be used in a wide range of applications.