News from the AI & ML world
DeeperML - #agenticai
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Jaime Hampton@AIwire
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AIwire
Microsoft has announced a new AI-powered orchestration system designed to revolutionize cancer care planning. This system, accessible through the Azure AI Foundry Agent Catalog, utilizes a healthcare agent orchestrator to consolidate and analyze diverse medical data sources such as imaging, genomics, clinical notes, and pathology. The goal is to assist clinicians in developing personalized treatment plans by streamlining the tumor board process, which traditionally involves in-depth reviews of patient records by multidisciplinary specialists.
The current tumor board model, while effective, is resource-intensive and only available to a small fraction of patients worldwide. Microsoft's AI-driven solution seeks to democratize access to comprehensive cancer care by automating key tasks within the tumor board workflow. The orchestrator employs both general-purpose and domain-specific AI agents to expedite processes that typically take hours. These agents can review and summarize medical images, pathology slides, and electronic health record (EHR) data, assess cancer stages according to established guidelines, identify relevant clinical trials, and compile current medical research into actionable reports. The AI system is designed to integrate seamlessly into platforms already used by clinicians, including Microsoft Teams, Word, and the broader Microsoft 365 suite. Early adopters of the technology include leading healthcare institutions such as Stanford Health Care, Johns Hopkins, Providence Genomics, and UW Health. Clinicians at Stanford are already using foundation model-generated summaries during tumor board meetings to reduce data fragmentation and surface insights that were previously difficult to access. The new healthcare agent orchestrator holds the potential to transform cancer care planning by enhancing efficiency, improving data accessibility, and ultimately enabling more personalized treatment strategies. Recommended read:
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Ken Yeung@Ken Yeung
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Microsoft is significantly expanding its AI capabilities to the edge, empowering developers with tools to create innovative AI agents. This strategic move, unveiled at Build 2025, focuses on enabling smarter and faster experiences across various devices. Unlike previous strategies centered on single-use AI assistants, Microsoft is now emphasizing dynamic agents that seamlessly integrate with third-party systems through the Model Context Protocol (MCP). This shift aims to create broader, integrated ecosystems where agents can operate across diverse use cases and integrate with any digital infrastructure.
Microsoft is empowering developers by offering the OpenAI Responses API, which allows the combination of MCP servers, code interpreters, reasoning, web search, and RAG within a single API call. This capability enables the development of next-generation AI agents. Among the announcements at Build 2025 were a platform to build on-device agents, the ability to bring AI to web apps on the Edge browser, and developer capabilities to deploy bots directly on Windows. The company hopes the developments will lead to broader use of AI technologies and a significant increase in the number of daily active users. Microsoft is already demonstrating the impact of its agentic AI platform, Azure AI Foundry, in healthcare, including streamlining cancer care planning. In addition to their AI initiatives, Microsoft has introduced a new AI-powered orchestration system that streamlines the complex process of cancer care planning. This orchestration system, available through the Azure AI Foundry Agent Catalog, brings together specialized AI agents to assist clinicians with the analysis of multimodal medical data, from imaging and genomics to clinical notes and pathology. Early adopters include Stanford Health Care, Johns Hopkins, Providence Genomics, and UW Health. Recommended read:
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@Salesforce
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Microsoft is aggressively pushing AI innovation to the edge, with a series of announcements highlighting the company's vision for an AI-powered future where humans partner with autonomous agents. At the Build developer conference, Microsoft unveiled tools designed to help developers build this agentic future, embedding bots into browsers, websites, operating systems, and everyday workflows. Unlike previous Copilot-centric approaches, Microsoft is placing greater emphasis on dynamic agents, powered by integrations with third-party systems through the Model Context Protocol (MCP), shifting from single-use AI assistants to broader, integrated ecosystems.
Microsoft is also introducing the Agent Store for Microsoft 365 Copilot, a centralized, curated marketplace designed to help automate tasks, streamline workflows, and boost productivity. The Agent Store offers a new experience within Microsoft 365 Copilot that enables users to browse, install, and try agents tailored to their needs, and features agents built by Microsoft, trusted partners, and customers. With over 70 agents available at launch, the Agent Store aims to make it easier to discover, share, and deploy agents across teams and organizations, using both low-code and pro-code tools. Furthermore, Microsoft’s agentic AI platform, Azure AI Foundry, is powering key healthcare advances with Stanford. Beyond healthcare, Microsoft is exploring ways to bring AI to web apps on the Edge browser and enable developers to deploy bots directly on Windows, as the company recognizes the full potential of its AI agents ecosystem is still unfolding. Recommended read:
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@www.bigdatawire.com
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Starburst is enhancing its data platform with new agentic AI capabilities, marking a significant step in the evolution of enterprise AI. These updates include the introduction of Starburst AI Workflows, a suite designed to expedite AI experimentation and deployment, alongside Starburst AI Agent, a pre-built natural language interface for the platform. This move aims to empower data analysts and application-layer AI agents, enabling them to extract faster and more insightful business insights from the data lakehouse. Starburst is also launching Starburst Data Catalog, a modern enterprise-grade metastore solution purpose-built to replace Hive Metastore in Starburst Enterprise.
The new AI capabilities address the growing need for enterprises to leverage AI for better-informed decision-making and increased efficiency. With fragmented data spread across various clouds and formats, many organizations struggle to build effective AI workflows. Starburst's platform updates aim to remove the friction between data and AI, enabling enterprise data teams to rapidly build AI applications and analytics on a single, governed foundation. Starburst uniquely helps enterprises speed up AI adoption, reduce costs, and realize value faster by enabling access to all their data, no matter where it lives, across clouds, on-premises, or hybrid environments, so they don’t need to move or migrate it to build, train, or tune their AI models. Justin Borgman, CEO and co-founder of Starburst, emphasized that AI's power is directly tied to the data it can access. The company's aim is to remove the barriers between data and AI by bringing distributed, hybrid data lakeside capabilities, enabling enterprise data teams to rapidly build AI and analytics on a governed foundation. Kevin Petrie, an analyst at BARC U.S., noted the significance of Starburst's new tools, highlighting their focus on addressing key risks associated with AI development, such as data access, quality, privacy, and incompatible systems. Recommended read:
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Michael Nuñez@AI News | VentureBeat
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techstrong.ai
, AI News | VentureBeat
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Microsoft has significantly expanded its Copilot Studio platform at Build 2025, introducing multi-agent systems designed to revolutionize enterprise workflows. This expansion allows AI agents built with Copilot Studio, Microsoft 365, Azure AI Agents Service, and Azure Fabric to collaborate on complex business tasks, delegating tasks to each other to complete processes. According to Ray Smith, VP of AI Agents at Microsoft, this approach addresses the challenges of creating reliable processes within a single agent, improving maintainability, simplifying solution building, and enhancing overall reliability. An example scenario involves a Copilot Studio agent pulling sales data from a CRM, handing it to a Microsoft 365 agent to draft a proposal in Word, and then triggering another agent to schedule follow-ups in Outlook.
Microsoft is also committed to building an open agentic AI ecosystem, demonstrated by its support for Anthropic’s Model Context Protocol (MCP). GitHub and Microsoft have joined the steering committee for MCP, an open-source standard designed to enable AI tools to directly access data systems. This initiative aims to simplify data connections for AI models, allowing them to fetch information from apps and data stores more easily and safely. The MCP allows any AI client to communicate with any MCP server through a standard interface, eliminating the need for custom connectors for each data source. Microsoft plans to add first-party support across Azure and Windows to assist developers in exposing app features as MCP servers. Furthering its commitment to open AI, Microsoft is making available a Model Router and a Model Leaderboard to optimize model selection for specific tasks. The Model Router enables real-time selection of the best model for a given query, while the Model Leaderboard ranks AI models based on their performance across various categories. Additionally, Microsoft is offering pre-built agents, custom agent building blocks, and a software development kit (SDK) based on MCP. The company is also extending the observability capabilities of its Azure AI Foundry to agents, offering comprehensive tools and platforms to foster an open and collaborative AI ecosystem, referred to as the Agentic Web. Recommended read:
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@www.salesforce.com
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Salesforce
, Salesforce
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Salesforce is placing significant emphasis on data security as it rolls out its AI agent implementations. According to recent research, a strong data foundation and robust governance capabilities are critical for businesses to securely implement agentic AI. While IT security leaders are largely optimistic about the potential benefits of AI agents, a majority acknowledge that there are significant readiness gaps in deploying the necessary security safeguards. This highlights the importance of prioritizing security measures as AI adoption accelerates across various industries.
As AI becomes more prevalent and cyber threats continue to evolve, a considerable number of IT security leaders recognize the need to transform their security practices. In fact, nearly 8 in 10 acknowledge that their security protocols require upgrades to address the challenges posed by AI. This transformation includes building AI fluency within organizations, redesigning workflows to incorporate AI agents effectively, and ensuring adequate human supervision to mitigate risks and maintain ethical standards. It's also worth noting that Salesforce's own data indicates unanimous optimism among IT leaders regarding the potential of AI agents. Salesforce is actively developing and acquiring technologies to enhance its Agentforce platform and empower developers to build secure and effective AI agents. The acquisition of Convergence, an AI automation startup, will integrate advanced AI agent design and automation capabilities into Agentforce. Furthermore, the launch of the Salesforce Developer Edition, which includes access to Agentforce and Data Cloud, provides developers with the tools to create, customize, and deploy autonomous AI agents with appropriate guardrails. By emphasizing secure data handling and governance, Salesforce aims to enable businesses to leverage the benefits of AI agents while minimizing potential security risks. Recommended read:
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@devops.com
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devops.com
, techstrong.ai
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Agentic Process Automation (APA) is rapidly transforming the landscape of software development, signaling a shift from Robotic Process Automation (RPA) towards more intelligent and adaptable automation solutions. This transition, highlighted at Automation Anywhere's Imagine conference in Orlando, indicates a future where businesses will leverage automation in unprecedented ways. APA, powered by agentic AI, overcomes the limitations of traditional RPA by enabling automation agents to understand context, learn from experiences, and dynamically adapt workflows. This advancement promises to redefine development, particularly for low-code/no-code and citizen developers.
The rise of APA means individuals with limited coding experience can now navigate complexities more easily, as APA-driven tools offer unprecedented flexibility and intelligence. Automation Anywhere is going "all in" on APA, which utilizes a Process Reasoning Engine developed with generative AI technologies. This allows APA agents to analyze, adapt, and respond to situations without constant human intervention, enhancing performance by integrating true intelligence into automation workflows. This intelligent automation is especially beneficial in software development environments dominated by low-code/no-code tools. However, the adoption of agentic AI also requires a focus on building AI fluency within organizations, redesigning workflows to accommodate AI agents, and ensuring proper supervision. While AI agents can act as competent virtual assistants, sifting through data, working across platforms, and producing real-time insights, they also demand testing, training, and guidance. Humans will need to occupy a supervisory role, ensuring adherence to central governance frameworks, maintaining ethical and security standards, and aligning decisions with broader company strategic goals, fostering a symbiotic relationship between humans and machines to balance autonomy with risk mitigation. Recommended read:
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@aithority.com
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AiThority
, AI News | VentureBeat
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Agentic AI is rapidly transforming workflow orchestration across various industries. The rise of autonomous AI agents capable of strategic decision-making, interacting with external applications, and executing complex tasks with minimal human intervention is reshaping how enterprises operate. These intelligent agents are being deployed to handle labor-intensive tasks, qualitative and quantitative analysis, and to provide real-time insights, effectively acting as competent virtual assistants that can sift through data, work across platforms, and learn from processes. This shift represents a move away from fragmented automation tools towards dynamically coordinated systems that adapt to real-time signals and pursue outcomes with minimal human oversight.
Despite the potential benefits, integrating agentic AI into existing workflows requires careful consideration and planning. Companies need to build AI fluency within their workforce through training and education, highlighting the strengths and weaknesses of AI agents and focusing on successful human-AI collaborations. It is also crucial to redesign workflows to leverage the capabilities of AI agents effectively, ensuring that they are integrated into the right processes and roles. Furthermore, organizations must not neglect supervision, establishing a central governance framework, maintaining ethical and security standards, fostering proactive risk response, and aligning decisions with wider company strategic goals. American business executives are showing significant enthusiasm for AI agents, with many planning substantial increases in AI-related budgets. A recent PwC survey indicates that 88% of companies plan to increase AI-related budgets in the next 12 months due to agentic AI. The survey also reveals that a majority of senior executives are adopting AI agents into their companies, reporting benefits such as increased productivity, cost savings, faster decision-making, and improved customer experiences. However, less than half of the surveyed companies are rethinking operating models, suggesting that there is still untapped potential for leveraging AI agents to fundamentally reshape how work gets done. Recommended read:
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Imran Aftab,@AI News | VentureBeat
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Tech leaders are rapidly deploying agentic AI, a cutting-edge technology poised to revolutionize the work ecosystem. These dynamic AI agents are designed to augment human decision-making, offering solutions to complex challenges across various industries. Companies are exploring these AI agents, recognizing their potential to take over labor-intensive tasks and provide real-time insights. However, the implementation requires careful consideration of training, testing, and guidance, similar to onboarding new employees.
Despite the enthusiasm surrounding agentic AI, concerns regarding security readiness persist. IT security leaders acknowledge the potential benefits of AI agents but express significant readiness gaps in deploying proper safeguards. To effectively manage these systems, a "human-in-the-loop" approach is crucial, ensuring accountability and risk mitigation. Organizations must establish collaborative frameworks that balance autonomy with robust oversight, aligning AI decisions with broader strategic goals and maintaining ethical standards. To harness the full potential of agentic AI, organizations must prioritize AI fluency among their workforce. AI upskilling remains under-prioritized, with a surprisingly small percentage of staff receiving adequate training. Fostering a culture that embraces technological change through regular training about agentic AI, and highlighting its strengths and weaknesses is essential. Furthermore, role-based training can empower employees across different capacities to effectively utilize generative AI, promoting successful human-AI collaborations and ensuring that the workforce is prepared for this technological shift. Recommended read:
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@Salesforce
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Salesforce is aggressively expanding its Agentforce platform to capitalize on the growing demand for AI-powered digital labor. A key component of this strategy is the acquisition of UK-based AI automation startup, Convergence.ai. This acquisition will bring Convergence's team and technology, including their expertise in AI agent design, autonomous task execution, and adaptive systems, into the Agentforce platform. The move is intended to accelerate the development of sophisticated AI agents capable of handling complex digital workflows within enterprise environments.
Convergence.ai, founded in 2024 by machine learning scientists Marvin Purtorab and Andy Toulis, has developed innovative AI agents, including "Proxy," designed to learn, evolve, and collaborate with humans. Their technology enables AI agents to navigate and execute tasks within dynamic digital systems, adapting to challenges such as pop-ups, system errors, and user interface changes. According to Salesforce, this acquisition will enable Agentforce to deliver AI that truly transforms how work gets done, allowing for AI assistants that can intelligently manage intricate tasks with human-like ingenuity. To further drive adoption of Agentforce, Salesforce has introduced new flexible pricing. The new ‘Flex Credits’ pricing model is designed to enable businesses to scale AI-powered digital labor to every employee, department, and process. This initiative aims to meet the rapidly accelerating demand for digital labor and positions Salesforce as a leader in the evolving landscape of AI-driven automation and customer interaction. Salesforce executives envision a future where Agentforce empowers customers with AI agents that not only follow instructions but also perceive, reason, and adapt to the complexities of modern digital workflows. Recommended read:
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@blogs.microsoft.com
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Microsoft is making significant moves in the realm of agentic AI and open-source technologies. In a strategic shift to invest more in AI-centric solutions and streamline operations, the company has been actively involved in initiatives ranging from open-sourcing key development tools to integrating AI into enterprise platforms. One notable example is the open-sourcing of Pyrefly, a faster Python type checker written in Rust, aimed at helping developers catch errors before runtime. This aligns with Microsoft's broader efforts to enhance developer productivity and contribute to the open-source community.
Microsoft is also focusing on integrating AI into enterprise solutions, highlighted by Salesforce CEO Marc Benioff mentioning the company’s Agentforce platform. The company is also cutting around 7,000 jobs, mostly in middle management and non-technical roles. This decision reflects a strategic reallocation of resources towards AI infrastructure. Microsoft plans to invest heavily in data centers designed for training and running AI models, signaling a major push into AI-driven technologies. Despite strong earnings, Microsoft is trimming its workforce to free up resources for AI investments. The layoffs, primarily affecting middle managers and support staff, are part of a broader industry trend where companies are streamlining operations to accelerate product cycles and reduce bureaucracy. While Microsoft shuts off Bing Search APIs and recommends switching to AI , Twilio has partnered with Microsoft to expand AI capabilities. Recommended read:
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@www.aiwire.net
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www.aiwire.net
, BigDATAwire
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SAS is making a significant push towards accountable AI agents, emphasizing ethical oversight and governance within its SAS Viya platform. At SAS Innovate 2025 in Orlando, the company outlined its vision for intelligent decision automation, highlighting its long-standing work in this area. Unlike other tech vendors focused on quantity, SAS CTO Bryan Harris stresses the importance of decision quality, arguing that the value of decisions to the business is the key metric. SAS defines agentic AI as systems that blend reasoning, analytics, and embedded governance to make autonomous decisions with transparency and human oversight when needed.
SAS views Large Language Models (LLMs) as valuable but limited components within a broader AI ecosystem. Udo Sglavo, VP of applied AI and modeling R&D at SAS, describes the agentic AI push as a natural evolution from the company's consulting-driven past. SAS aims to take its extensive IP from solving similar challenges repeatedly and incorporate it into software products. This shift from services to scalable solutions is accelerated by increased customer comfort with prepackaged models, leading to wider adoption of agent-based systems. SAS emphasizes that LLMs are only one piece of a larger entity, stating that decision quality and ethical considerations are paramount. Bryan Harris noted that LLMs can be unpredictable, which makes them unsuitable for high-stakes applications where auditability and control are critical. The focus on accountable AI agents ensures that enterprises can deploy AI systems that act autonomously while maintaining the necessary transparency and oversight. Recommended read:
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@yseop.com
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Data Phoenix
, Madrona
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The rise of AI agents is gaining momentum, with companies like Nekuda, WisdomAI, and even web browser pioneer Opera making significant strides in the field. AI agents are designed to perform tasks autonomously, signaling a shift towards more intelligent and versatile AI applications. Madrona is backing Nekuda to build a payment stack specifically for AI agents, enabling trusted and scalable agent-driven commerce. The evolution from rigid workflows to dynamic AI agents is being championed by companies like Yseop, who are focused on AI-driven orchestration where AI collaborates with humans.
WisdomAI has secured $23 million in funding to launch its "Agentic Data Insights Platform." This innovative platform uses AI agents powered by a "Knowledge Fabric" to revolutionize business intelligence. The Knowledge Fabric maps an organization's data ecosystem, enabling AI agents to curate knowledge, provide instant answers, and proactively identify insights. Unlike traditional business intelligence systems that rely on static dashboards and specialized skills, WisdomAI's platform aims to deliver insights actively without requiring extensive technical expertise. Companies are recognizing the potential of AI agents to reshape various industries. Visa has reported a significant increase in traffic to retail sites from AI agents, highlighting their growing influence in commerce. Furthermore, Opera is integrating AI agents into its web browser, potentially reshaping online experiences. The convergence of AI, automation, and user experience signifies a new era where AI agents become active collaborators, making smarter decisions and driving productivity across diverse sectors. Recommended read:
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@hbr.org
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The Dataiku Blog
, Smashing Frames
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SAS has unveiled its roadmap for agentic AI at SAS Innovate 2025 in Orlando, positioning itself as a company deeply rooted in intelligent decision automation. Agentic AI, defined as AI systems capable of acting autonomously to achieve goals without constant human intervention, has gained significant traction. SAS CTO Bryan Harris emphasized that the key metric isn't the quantity of AI agents deployed, but the quality and value of the decisions they facilitate within an enterprise. SAS's approach integrates reasoning, analytics, and embedded governance into AI systems.
SAS defines agentic AI beyond simple automation, focusing on systems that make decisions with a blend of reasoning, analytics, and embedded governance. The SAS Viya platform supports this by unifying deterministic models, machine learning algorithms, and large language models. This orchestration enables the deployment of intelligent agents capable of autonomous action when appropriate, while also providing transparency and human oversight when the stakes are high. Udo Sglavo, VP of applied AI and modeling R&D, highlights this as a natural progression from SAS's consulting-driven history, aiming to transform repeated problem-solving IP into scalable software solutions. The rising comfort with LLMs has accelerated the shift towards prepackaged models and agent-based systems. However, both Harris and Sglavo caution that LLMs are just one element of a larger ensemble. Agentic AI is also transforming the retail sector, enhancing personalization, optimizing supply chains, and accelerating product innovation. AI agents can serve as marketing assistants, delivering anticipatory and dynamic personalized recommendations. This is achieved by understanding changing consumer preferences, shopper browsing patterns, and adapting to real-time factors, ensuring individualized and effective marketing strategies. Recommended read:
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@www.microsoft.com
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The advent of "agentic AI" marks a significant shift in the technological landscape, characterized by AI agents capable of autonomous action. These agents, as seen in enterprise resource planning (ERP) systems, are being deployed to automate tasks, streamline processes, and ultimately redefine how work is done. While the term "agentic AI" might be new, the underlying concept of software agents isn't, tracing back to simple systems with a target function to optimize autonomously. The current excitement stems from the integration of these agents with modern AI systems, enabling them to extract data, interact with systems, and potentially offer new modes of operation within businesses.
The integration of AI agents into business processes, particularly within ERP systems like Microsoft Dynamics 365, is poised to revolutionize operations. Rather than just acting as assistants, these agents function as "digital colleagues," taking on specific tasks and transforming human-dependent processes into intelligent, scalable automation. Organizations are expecting these agents to be moderately or extensively integrated into their company’s AI strategy. This shift promises to reduce manual effort, improve accuracy, and accelerate decision-making across key areas such as finance, supply chain, and project operations. However, the rise of agentic AI is not without its critiques. Concerns are emerging about whether the increasing reliance on AI-generated content leads to a flattening of ideas and a regression toward mediocrity. There's a growing sentiment that as AI churns out structurally sound but uninspired content, the uniqueness and originality of human thought might be suppressed. Yet, conversely, agentic AI is also being recognized for its potential to empower the disenfranchised, lowering the barrier to entry for research and giving a voice to those who were previously unheard. This duality highlights the need for careful consideration of both the benefits and potential drawbacks as agentic AI continues to evolve. Recommended read:
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kevinokemwa@outlook.com (Kevin@windowscentral.com
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Microsoft CEO Satya Nadella is betting big on the future of Agentic AI, predicting a disruptive shift that could reshape the entire software landscape. Nadella envisions an "Agentic AI era" where traditional Software as a Service (SaaS) applications are rendered obsolete. In a recent discussion, Nadella questioned the necessity of conventional software like Excel, suggesting that Agentic AIs will replace business logic, fundamentally altering how organizations operate. Microsoft's CEO believes AI agents will take over tasks, interacting across multiple repositories, and ultimately collapsing the need for individual business applications.
Nadella's commitment to open architectures is evident through Microsoft's endorsement of Google DeepMind's Agent2Agent (A2A) open protocol and Anthropic's Model Context Protocol (MCP). This move signifies a shift from proprietary ecosystems towards cross-platform, agentic AI collaboration. By supporting these open protocols in Copilot Studio and Foundry, Microsoft aims to enable customers to build agentic systems that can seamlessly interoperate. This initiative is expected to accelerate the development and adoption of agentic-based apps and platforms, fostering innovation and interdependence within the AI community. Beyond software, Microsoft is exploring AI's potential to revolutionize energy production. The company believes that AI can accelerate the development of nuclear fusion as a practical energy source. Microsoft Research held its inaugural Fusion Summit to find ways to utilize AI to push the boundaries of nuclear fusion. Realizing the enormous energy needs of AI, the fusion research is intended to both bring clean energy to the world but also ensure the capacity to continue to expand AI capabilities. This would hasten our understanding of how to power AI, addressing the sustainability challenges associated with its growing energy consumption. Recommended read:
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Ken Yeung@Ken Yeung
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pub.towardsai.net
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HubSpot has launched a public beta of its Model Context Protocol (MCP) server, empowering developers to integrate AI applications with customer relationship management (CRM) data. This offering, provided as an NPM package, is specifically designed for developers, technical teams, and businesses seeking to create custom applications or integrations using large language models (LLMs). The MCP server allows AI applications like Claude and Cursor to directly access HubSpot data, facilitating tasks such as retrieving, creating, or modifying HubSpot objects, listing properties, and generating tasks and notes using natural language. This allows users to streamline development and gain insights, demonstrated by examples such as summarizing deals or updating customer information.
The Model Context Protocol standardizes the way AI models connect to applications through a consistent interface. HubSpot describes MCP as an abstraction layer over traditional APIs, enabling AI agents to access application functionality without needing to understand specific API protocols. This mirrors the function of API feeds from companies like Google, Facebook, and Salesforce, providing a standardized connection point in the AI era. By opening its MCP server, HubSpot aims to equip small and medium-sized businesses (SMBs) with the tools to leverage AI in a manner previously accessible only to larger enterprises. Microsoft CEO Satya Nadella has also endorsed the Model Context Protocol, signaling a shift towards open standards for AI interoperability. Nadella’s support, along with his endorsement of Google DeepMind's Agent2Agent (A2A) protocol, is expected to accelerate AI-based collaboration and the development of agentic AI applications. He announced upcoming support for A2A and MCP in Copilot Studio and Foundry, emphasizing that open protocols are key to enabling the agentic web. This move aligns with Nadella's history of championing open, interoperable AI architectures, promoting the idea that open standards, rather than proprietary silos, drive the adoption of new AI technologies. Recommended read:
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Chris McKay@Maginative
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Microsoft is formally partnering with Google on the Agent2Agent (A2A) protocol, signaling a major step towards interoperability in AI systems. This move will enable AI agents to communicate and collaborate across different platforms and organizational boundaries, a previously significant limitation of current AI technology. Microsoft is integrating the A2A protocol into its Azure AI Foundry and Copilot Studio, allowing enterprise customers to build multiagent workflows that span partner tools and production infrastructure. This partnership reflects the industry’s recognition that achieving seamless AI collaboration requires shared standards and open protocols.
The endorsement of Google DeepMind's A2A protocol and Anthropic’s Model Context Protocol (MCP) by Microsoft CEO Satya Nadella is expected to accelerate the development and adoption of agentic AI. Nadella's support is seen as a catalyst for further collaboration within the AI community, potentially leading to the creation of new agentic-based applications and platforms. He highlighted the importance of open protocols like A2A and MCP for enabling an agentic web, emphasizing that these standards allow customers to build agentic systems that interoperate by design. This commitment to open architectures aligns with Nadella’s long-standing belief that open standards are crucial for driving the adoption of new AI technologies. Microsoft is joining over 50 technology partners, including Salesforce, Oracle, and SAP, in supporting the A2A standard created by Google. The company is also actively contributing to the A2A working group on GitHub, a Microsoft subsidiary, to further develop the specification and tooling. The A2A protocol allows AI agents to securely exchange goals, manage state, invoke actions, and return results, facilitating complex workflows across multiple specialized agents. Microsoft’s commitment to A2A underscores the shift towards an "open garden" approach, where AI systems can effectively work together regardless of their origin, marking a foundational shift in how software is built and decisions are made. Recommended read:
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