Now we are on the topic of DeAI and AI Agents, here’s this weeks roundup to get you up to speed:s4mmy (@S4mmyEth):https://t.co/lE0LBr5qiA ...

Core Insights - GPTBots.ai, the enterprise AI platform of Aurora Mobile, co-hosted a thematic course titled "AI Agents Drive Intelligent Transformation" with the Hong Kong Productivity Council, focusing on the practical applications of AI Agents in business scenarios [1][5][14] - The event aimed to provide Hong Kong enterprises with actionable strategies to overcome transformation challenges and embrace AI innovation, contributing to the growth of the digital economy in Hong Kong [3][12] Company Overview - GPTBots.ai serves over 60,000 enterprises and developers globally, offering a suite of capabilities including multi-model integration, no-code agent building, and enterprise-grade security [13] - The platform emphasizes low barriers to entry and adaptability, enabling enterprises to integrate AI into core business processes effectively [5][7] Event Highlights - Keynote addresses included insights from Tommy Lui, Chief Business Officer of GPTBots, and Jerry Yin, Vice President of Aurora Mobile, discussing the evolution of AI Agents and the platform's architecture [5][7] - The event featured live demonstrations showcasing the practical implementation of AI Agents in various sectors, including finance, healthcare, and retail, highlighting their role in optimizing customer service and operational efficiency [9][12] Future Commitment - GPTBots plans to continue its collaboration with HKPC to develop further training programs aimed at empowering more Hong Kong enterprises in their journey towards intelligent transformation [14]

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Core Insights - Oracle has been recognized as Exemplary for product and consumer experiences in the 2025 ISG Research Buyers Guide for AI Agents and Conversational AI for Workforce [1] Group 1 - Oracle received top ratings across product experience, innovation, and customer value [1]

Group 1 - Google has launched a new open-source payment system designed to facilitate money transfers for AI applications, incorporating payment options such as credit cards, debit cards, and stablecoins [1][2] - The payment system was developed in collaboration with Coinbase, the Ethereum Foundation, and over 60 other companies, including American Express and Salesforce [1][2] - The system is built to support both traditional payment methods and emerging technologies like stablecoins, ensuring interoperability with Coinbase's payment mechanisms [2] Group 2 - The launch of the payment system follows a communications protocol introduced by Google in April, aimed at enabling AI agents to communicate and transfer funds securely [3] - The GENIUS Act has created a favorable regulatory environment for stablecoins in the U.S., prompting increased interest from major tech companies [4] - The stablecoin market is projected to grow significantly, with estimates suggesting a market cap of $1.2 trillion by the end of 2028 and over $400 billion by the end of 2025 [6]

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Core Viewpoint - The article discusses the evolution and differentiation between AI Agents and Agentic AI, highlighting their respective roles in automating tasks and collaborating on complex objectives, with a focus on the advancements since the introduction of ChatGPT in November 2022 [2][10][57]. Group 1: Evolution of AI Technology - The development of AI technology has progressed from early expert systems like MYCIN to modern AI Agents and Agentic AI, marking a significant paradigm shift in capabilities [10][11]. - ChatGPT's release in November 2022 is identified as a pivotal moment that catalyzed the evolution of AI Agents, transitioning from passive responders to more autonomous systems capable of executing multi-step tasks [12][24]. - The introduction of frameworks like AutoGPT and BabyAGI in 2023 signifies the formal establishment of AI Agents, which integrate LLMs with external tools to perform complex tasks [12][24]. Group 2: Characteristics of AI Agents - AI Agents are defined as modular systems driven by LLMs and LIMs, designed for task automation, filling the gap where generative AI lacks execution capabilities [13][16]. - Three core features distinguish AI Agents from traditional automation scripts: autonomy, task-specificity, and reactivity [16][17]. - The integration of tools allows AI Agents to overcome limitations of static knowledge and hallucination issues, enabling them to perform real-time data retrieval and processing [19][20]. Group 3: Agentic AI and Multi-Agent Collaboration - Agentic AI represents a shift towards multi-agent collaboration, where multiple AI Agents work together to achieve complex goals, enhancing system-level intelligence [24][27]. - The architecture of Agentic AI includes dynamic task decomposition and shared memory, facilitating efficient collaboration among specialized agents [33][36]. - Real-world applications of Agentic AI demonstrate its advantages in various fields, such as healthcare and agriculture, where multiple agents coordinate to optimize processes [37][38]. Group 4: Challenges and Future Directions - Both AI Agents and Agentic AI face challenges, including causal reasoning deficits and coordination issues among multiple agents [48][50]. - Proposed solutions include enhancing retrieval-augmented generation (RAG), implementing causal modeling, and establishing shared memory architectures to improve collaboration and decision-making [49][53]. - The future roadmap emphasizes the need for deeper causal reasoning, transparency in decision-making, and ethical governance to ensure the responsible deployment of AI technologies [56][59].

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