Core Insights - The report outlines the practical guide for building Agentic AI applications, emphasizing its role as an autonomous software system based on large language models (LLMs) that can automate complex tasks through perception, reasoning, planning, and tool invocation [1][5]. Group 1: Agentic AI Technology Architecture and Key Technologies - Agentic AI has evolved from rule-based engines to goal-oriented architectures, with core capabilities including natural language understanding, autonomous planning, and tool integration [3][5]. - The technology architecture consists of single-agent systems for simple tasks and multi-agent systems for complex tasks, utilizing protocols for agent communication and tool integration [3][4]. Group 2: Building Solutions and Scenario Adaptation - Amazon Web Services offers three types of building solutions: dedicated agents for specific tasks, fully managed agent services, and completely self-built agents, allowing enterprises to choose based on their needs for task certainty and flexibility [1][4]. - The report highlights various application scenarios, such as optimizing ERP systems and automating document processing, showcasing the effectiveness of Agentic AI in reducing manual operations and improving response times [4][5]. Group 3: Industry Applications and Value Validation - Case studies include Kingdee International's ERP system optimization and Formula 1's root cause analysis acceleration, demonstrating the practical benefits of Agentic AI in different sectors [2][4]. - The manufacturing and financial sectors are also highlighted for their use of Agentic AI in automating contract processing and generating visual reports, respectively, which enhances decision-making efficiency [4][5]. Group 4: Future Trends and Challenges - The report discusses future trends indicating that Agentic AI will penetrate various fields, driven by advancements in model capabilities and standardized protocols [5]. - Challenges include ensuring the stability of planning capabilities, improving multi-agent collaboration efficiency, and addressing the "hallucination" problem in output credibility [4][5].

Core Insights - The article emphasizes that multi-agent AI represents the next significant direction for large models, showcasing unprecedented capabilities and indicating a major iteration in large language models (LLMs) [1][3][9] - Amazon Web Services (AWS) is leading the charge with a comprehensive Agentic AI technology stack, facilitating the transition from concept to practical application [10][62] Group 1: Multi-Agent AI Developments - Recent releases like Grok 4 and Kimi K2 utilize multi-agent technology, enabling models to autonomously understand their task environment and utilize external tools to solve complex problems [2][4] - AWS's Agentic AI framework includes four pillars: model application capability, security and reliability, scalability, and deployment and production capability [5][6] - The introduction of Amazon Bedrock AgentCore allows for the construction and deployment of enterprise-level secure agent services through seven core services [14][17] Group 2: Agent Applications and Tools - The AgentCore Runtime provides a unique runtime environment for agent applications, supporting third-party models and significantly reducing deployment costs [20][21] - AWS has expanded its Amazon Bedrock platform to include 12 major model vendors, enhancing its capabilities in generative AI across various modalities [24][27] - The launch of Amazon S3 Vectors reduces vector storage and query costs by 90%, enabling agents to retain more context from interactions [50][52] Group 3: Collaboration and Development - The Strands Agents SDK has been upgraded to facilitate the creation of multi-agent systems, allowing for more efficient collaboration on complex tasks [38][39] - New protocols like Agent to Agent (A2A) enhance communication between agents, marking a shift towards proactive collaboration [41][46] - The introduction of various APIs and tools within Strands Agents V1.0 simplifies the development of multi-agent applications, lowering the barrier for developers [45][46] Group 4: Future Outlook - The article predicts that by 2025, agents will begin large-scale deployment, fundamentally changing how software interacts with the world and how humans interact with software [9][61] - AWS aims to create the most practical Agentic AI platform, supporting companies of all sizes in deploying reliable and secure agent solutions [62][63] - The ongoing evolution of agent technology is expected to lead to more disruptive applications, enhancing the integration of AI as a digital colleague in business operations [64][65]

Workshop Overview - The workshop focuses on building AI agents using Amazon's agent technologies [1] - Participants will gain hands-on experience in building sophisticated AI agents [1] - The workshop is 2-hour long [1] Technologies Highlighted - Amazon Nova Act is used for reliable web navigation [1] - Model Context Protocol (MCP) connects agents to external data sources and APIs [1] - Amazon Bedrock Agents orchestrates complex workflows [1] Skills Acquired - Participants will learn to build agents that can navigate the web like humans [1] - Participants will learn to perform complex multi-step tasks [1] - Participants will learn to leverage specialized tools through natural language commands [1]