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AI Governance & Legal Implications - Explores the legal and societal implications of AI agents acting autonomously [1] - Discusses trust in a world increasingly shaped by autonomous agents [1] Technological Advancement - Focuses on the transition from AI assistance to AI agency [1] - Examines the impact of AI on law, technology, and trust [1]

Core Viewpoint - Magnesium Technology submitted its prospectus to the Hong Kong Stock Exchange on June 25, 2025, projecting a revenue of 930 million yuan in 2024 with a compound annual growth rate (CAGR) of 43% [1][2]. Group 1: Company Overview - Magnesium Technology is a competitive autonomous intelligent agent supplier in China's robotics application sector, focusing on smart laboratories and intelligent manufacturing to enhance productivity and drive industrial transformation [2]. - The company has developed multifunctional autonomous intelligent agent solutions leveraging proprietary technology in robotics automation and AI [2]. Group 2: Market Potential - The global autonomous intelligent agent robotics market is rapidly growing, from approximately 31.8 billion yuan in 2020 to an estimated 114.3 billion yuan in 2024, with a CAGR of 37.7%. It is projected to further expand to about 383.7 billion yuan by 2030 at a CAGR of 22.4% [4]. - The application of autonomous intelligent agents in smart laboratories and intelligent manufacturing is still in its early development stages, indicating significant potential for market penetration as traditional systems transition to the AI era [4]. Group 3: Financial Performance - Magnesium Technology ranked first among autonomous intelligent agent suppliers in China based on revenue from the smart laboratory sector in 2024 [5]. - The company's revenue figures for 2022, 2023, and 2024 were 455 million yuan, 663 million yuan, and 930 million yuan, respectively, reflecting a CAGR of 43% [5]. - As of June 21, 2025, the company's order backlog increased significantly to 1.5 billion yuan, demonstrating strong business expansion capabilities [5].

Pigment's Business and Technology - Pigment is an enterprise planning and performance management platform that helps companies build strategic plans and adapt to changing market conditions [1] - Pigment AI consists of conversational AI and autonomous agents that accelerates insight generation and scenario creation across the organization [2] - Pigment's autonomous agents framework allows users to schedule and automate reports and scenario creation, saving hundreds of hours of manual work [3] Challenges with Previous AI Architecture - Linear chain pipelines limited flexibility and made experimentation with agent-based workflows complex and cumbersome [4] - Managing graphs, memory, state transitions, and interruptions for custom agents was too complex [5] - Strong control over tools and agents, simple state management, and asynchronous processing were critical needs for financial use cases [5] Benefits of Long Graph - Long Graph offers graph-based orchestration, long-term memory, streaming, and interrupt capabilities [6] - Graph orchestration is easy to set up, allowing easy definition and tweaking of agent iteration and collaboration [6] - Full visibility and control over message flow between agents enables building reliable and testable logic [7] - Agent topologies can be abstracted into configuration files, enabling rapid prototyping and deployment of new workflows [7] Impact of Long Graph - Reduced time to insight from hours to seconds using natural language search and agent analysis [8] - Faster decision-making by surfacing anomalies and key performance gaps in real time [8] - Users can focus on higher value work by automating routine analysis and planning tasks [9] - Engineering team has more time to experiment and innovate, focusing on higher impact features [9] - Significantly less time is spent implementing key site capabilities like persistent, long-term memory [9]

Industry Focus: Autonomous Agents in Healthcare - The workshop explores building autonomous agents for managing complex processes like multi-day medical treatments [1] - The system aims to help patients self-administer medication regimens at home [1] - A key challenge is enabling agents to adhere to protocols while handling unexpected patient situations [1] Technology Stack - The solution utilizes a hybrid system of code and prompts, leveraging LLM decision-making to drive a web application, message queue, and database [1] - The stack includes LangGraph/LangSmith, Claude, MCP, Nodejs, React, MongoDB, and Twilio [1] - Treatment blueprints, designed in Google Docs, guide LLM-powered agents [1] Agent Evaluation and Human Support - The system incorporates an agent evaluation system using LLM-as-a-judge to assess interaction complexity [1] - The evaluation system escalates complex interactions to human support when needed [1] Key Learning Objectives - Participants will learn how to build a hybrid system of code and prompts that leverages LLM decisioning [1] - Participants will learn how to design and maintain flexible agentic workflow blueprints [1] - Participants will learn how to create an agent evaluation system [1]