A new way to learn AI Agent скачать в хорошем качестве

A new way to learn AI Agent

What are AI agents? How will we use AI agents? What are the use cases of AI agents for humans and enterprises? This video provides an overview of AI agents, defining them as interactive partners that help answer complex questions and complete tasks autonomously (0:28-0:56). Key takeaways from the video: What are AI Agents? They go beyond traditional chatbots, incorporating reasoning, action, and decision-making capabilities (0:46-0:56). The speaker likens them to a combination of LLM + tools + memory + actions (0:52). Real-world Examples: The video illustrates AI agents with examples like booking flights and hotels, scheduling doctor appointments, searching for jobs, and assisting HR with employee onboarding (1:43-2:00). Industry Adoption: The speaker highlights that 30% of enterprise software integrations now include AI agents due to their ability to automate 50% of daily work decisions (2:03-2:27). Types of AI Agents: Various types of AI agents are being adopted across industries, including customer agents, employee agents, creative agents, data agents, code agents, security agents, research agents, and developer agents (2:31-2:59). Agent Development Kit (ADK): The ADK is a Python library that simplifies the creation of single and multi-agent systems. It supports communication history, shared state, session memory, debugging tools, evaluation, and deployment to an agent engine (4:06-5:01). Agent Engine: This is a Google Cloud-managed runtime environment for deploying AI agents, offering automated autoscaling and fully managed infrastructure (5:14-5:29). Multi-Agent Systems: These involve the orchestration and communication between multiple AI agents to complete complex tasks, such as a flight booking agent communicating with a hotel booking agent (5:31-5:58). Frameworks for AI Agents: The video mentions Google ADK, LangGraph, AutoGen, and Small Agent as available frameworks for developing AI agents (6:39-6:57). Low-level orchestration can be done using LangChain and GenKit (7:01-7:05). Building and Deployment: AI agents can be built using conversional AI agent models or enterprise AI agent solutions, often leveraging open-source tools with the Google Agent Engine and ADK for deployment (7:25-8:02).