Core Insights - Major tech giants are collaborating to establish a unified infrastructure for artificial intelligence, aiming to break down interoperability barriers between AI agents and enterprise applications [1][2] - The newly formed "Agentic Artificial Intelligence Foundation" will focus on developing open-source standards to ensure AI agents can work seamlessly across platforms [1][3] - This initiative is likened to the establishment of interbank electronic payment standards, facilitating smoother integration of AI automation into enterprise software [1][2] Industry Collaboration - The foundation will be organized by the Linux Foundation, indicating a rare collaboration among competitors in the tech industry at the foundational protocol level [1][3] - The collaboration is seen as a positive signal for industry maturity, as tech giants choose to work together to expand the market rather than compete [3] Standardization Efforts - The foundation will initially focus on standardizing three existing open-source AI tools, including connection protocols, instruction formats, and local running agents [4] - Key tools include: - Model Context Protocol (MCP) developed by Anthropic, which standardizes communication between AI models and APIs [4] - Agents.md by OpenAI, which standardizes the instruction format for coding agents [4] - Goose by Block, an open-source AI agent that can run locally without internet dependency [4] Security Challenges - Despite the acceleration of standardization, enterprise applications face significant security challenges, particularly concerning "prompt injection attacks" [6] - Companies are increasingly aware of the risks associated with connecting AI agents to operational tools, emphasizing the need for improved security measures [6] - The newly established foundation aims to address these security and technical collaboration issues to pave the way for large-scale commercialization of AI agents [6]

Group 1 - The core idea of the news is the establishment of the AI Agent Foundation (AAIF) by leading companies to create open-source technical standards related to AI agents, addressing the growing conflict between AI-based tools and the internet ecosystem [1][2] - The AAIF is operated under the Linux Foundation, ensuring that the development of technical standards is not controlled by individual companies, similar to the standardization efforts in cross-bank electronic payments [1] - Founding projects of the AAIF include Anthropic's MCP protocol, OpenAI's AGENTS.md design blueprint, and Block's open-source AI agent Goose, with significant participation from major tech companies like Google, Microsoft, and Amazon [2][3] Group 2 - The MCP protocol, released by Anthropic, provides a standardized way for AI models to connect to various data sources and tools, which is essential for achieving AI agent functionality [3] - Major tech companies, including Google, Microsoft, OpenAI, Alibaba, Tencent, and Baidu, have announced their support for the MCP protocol, although developers have reported issues, particularly regarding security vulnerabilities [3] - AGENTS.md is a format developed by OpenAI for instructing coding agents, while Goose is a locally run open-source AI agent developed by Block that does not require an internet connection [3]

Industry Recognition - Forbes 2025 CIO Next list honored Dhanji Prasanna [1] AI Development - Anthropic, Databricks, and Stripe utilize Goose, an open-source AI agent created by Dhanji Prasanna [1]

Dhanji Prasanna, an honoree on the Forbes 2025 CIO Next list, oversaw the creation of Goose, an open source AI agent used by Anthropic, Databricks and Stripe. https://t.co/cwRzfW1kt4 #ForbesCIO (Illustration By Oriana Fenwick For Forbes) https://t.co/Ony09Tg2fV ...

Dhanji Prasanna, an honoree on the Forbes 2025 CIO Next list, oversaw the creation of Goose, an open source AI agent used by Anthropic, Databricks and Stripe. https://t.co/cwRzfW1kt4 #ForbesCIO (Illustration By Oriana Fenwick For Forbes) https://t.co/Lr4CzwWoZ8 ...

Dhanji Prasanna, an honoree on the Forbes 2025 CIO Next list, oversaw the creation of Goose, an open source AI agent used by Anthropic, Databricks and Stripe. https://t.co/cwRzfW1kt4 #ForbesCIO (Illustration By Oriana Fenwick For Forbes) https://t.co/j6wZw7Mg5n ...

Core Insights - Block has successfully deployed AI agents to all 12,000 employees within eight weeks, showcasing its commitment to integrating AI into its operations [1] - The company, originally known as Square, Inc., has evolved from a payment service provider to a broader financial and blockchain ecosystem, rebranding as Block, Inc. in December 2021 [1] - Block's CTO, Dhanji R. Prasanna, emphasized the importance of becoming an "AI-native" company, which has led to significant organizational changes and a focus on technology [2][7] AI Integration and Tools - Block launched an open-source AI agent framework called "Goose" in early 2025, designed to connect large language model outputs with actual system behaviors, enabling automation and efficiency [2][18] - Teams using Goose have reported saving an average of 8 to 10 hours of manual work per week, with an estimated overall labor savings of 20% to 25% across the company [12][16] - Goose is fully open-source, allowing external users to download and utilize it, promoting a collaborative ecosystem [19][35] Organizational Changes - The transition from a General Manager structure to a functional structure has been pivotal in focusing on technology and AI integration, allowing engineers and designers to work under unified leadership [8][10] - The cultural shift towards viewing Block as a technology company rather than just a fintech firm has reinvigorated innovation and creativity within the teams [7][9] Future of AI in Engineering - The future of AI in engineering is expected to enhance productivity significantly, with the potential for AI to autonomously handle more complex tasks and improve decision-making processes [22][25] - The integration of AI tools is anticipated to blur the lines between different job roles, enabling non-technical teams to leverage AI for their tasks [29] Recruitment and Company Culture - Block is focusing on hiring individuals who embrace AI tools, fostering a "learning-first" culture that prioritizes experimentation and adaptation [26][27] - The company aims to maintain a balance between automation and human oversight, ensuring that AI complements human judgment rather than replacing it [25][28]

Core Insights - Block has successfully deployed AI agents to all 12,000 employees within eight weeks, showcasing its commitment to integrating AI into its operations [2] - The company, originally known as Square, Inc., has evolved from a payment service provider to a broader financial and blockchain ecosystem, rebranding as Block, Inc. in December 2021 [2] - The introduction of the open-source AI agent framework "Goose" aims to connect large language model outputs with actual system behaviors, enhancing productivity and automation [3][14] Company Background - Block was founded in 2009 by Jack Dorsey and Jim McKelvey, initially focusing on a mobile card reader to help small merchants accept credit cards [2] - The company went public in 2015 and has since expanded its services to approximately 57 million users and 4 million merchants in the U.S. by 2024 [2] AI Integration and Transformation - The CTO, Dhanji R. Prasanna, led a team of over 4,000 engineers to transform Block into one of the most AI-native large enterprises globally, driven by an "AI declaration" he wrote to the CEO [4][7] - The organizational shift from a General Manager structure to a functional structure was crucial for focusing on technology and AI development [10][11] - The changes have resulted in a unified technical focus, allowing engineers to collaborate more effectively and enhancing the overall technological depth of the company [12][13] Productivity Gains from AI - Teams utilizing Goose have reported saving an average of 8 to 10 hours of manual work per week, with an estimated overall labor savings of 20% to 25% across the company [14][17] - Goose serves as a cultural signal, enabling all employees to leverage AI for building and creating, thus integrating AI into the company's operational fabric [16] Goose AI Agent - Goose is a general-purpose AI agent that can perform various tasks, including organizing files, writing code, and generating reports, by connecting with existing enterprise tools [22][23] - The framework is built on the Model Context Protocol (MCP), allowing it to execute tasks in the digital realm, thus enhancing productivity [24][25] - Goose is open-source, enabling other companies to adopt and adapt the technology, promoting a collaborative ecosystem [27] Future of AI in Engineering - The future of AI in engineering is expected to enhance autonomy, allowing AI to work independently on tasks, potentially transforming how engineers approach coding and project management [31][32] - AI's role in automating processes is anticipated to evolve, with the possibility of AI optimizing growth and revenue generation, although human oversight will remain essential [34][35] Hiring and Organizational Strategy - The company is focusing on hiring individuals who embrace AI tools, fostering a culture of continuous learning and adaptation [36][37] - The integration of AI has led to a strategic shift in hiring practices, emphasizing structural optimization over mere expansion of the engineering team [39][40]

Core Insights - The article discusses the potential of Model Context Protocol (MCP) to revolutionize the interaction between AI agents and external tools, enabling seamless integration and automation of complex tasks [3][30] - MCP is positioned as an open standard that connects AI agents with necessary tools and data, addressing the fragmentation and integration challenges in the AI ecosystem [6][30] Understanding Model Context Protocol - MCP is an open standard based on JSON-RPC 2.0, facilitating communication between AI agents (hosts/clients) and external capabilities (servers) [4][6] - Key components of MCP include hosts (user-facing AI applications), clients (components managing communication), and servers (lightweight components exposing external functionalities) [6][7] Key Components of MCP - Agents can connect to MCP-compatible servers without writing custom code for each new API or service, enhancing interoperability and reducing integration complexity [5][6] - Standardized interfaces expose functionalities such as tools, resources, prompts, and sampling, allowing for modular development [6][10] Benefits of Standardization - MCP transforms the integration landscape from M×N complexity to M+N modularity, improving interoperability and future-proofing AI systems [11][18] - It democratizes tool development, enabling developers to create and share specialized tool servers [18][34] MCP Implementation: Case Studies - Block's "Goose" AI agent exemplifies MCP's application, integrating with various backend systems to enhance operational efficiency [14][33] - Development tools like Windsurf and Replit are adopting MCP to provide richer, context-aware coding assistance [17][33] Impact on Agent Capabilities - MCP enhances agent memory and state persistence, allowing for long-term memory and dynamic knowledge organization [26][28] - Agents can maintain context across multiple tool calls and manage persistent task states, facilitating complex workflows [28][29] Observed Applications and Adoption of MCP - MCP is gaining traction in real-world applications, standardizing interactions between AI agents and external data, tools, and services [29][30] - The open-source nature of MCP encourages community contributions and the development of a growing ecosystem of MCP servers [33][34]

Jack Dorsey has recently released two apps in less than a week.How? He's riding the vibe coding wave and using an AI tool called Goose to crank out new apps for messaging and tracking sun exposure. https://t.co/yOdYsmjBXX ...