News from the AI & ML world
DeeperML - #automation
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@www.bigdatawire.com
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The rise of Agentic AI is transforming enterprise workflows, as companies increasingly deploy AI agents to automate tasks and take actions across various business systems. Dust AI, a two-year-old artificial intelligence platform, exemplifies this trend, achieving $6 million in annual revenue by enabling enterprises to build AI agents capable of completing entire business workflows. This marks a six-fold increase from the previous year, indicating a significant shift in enterprise AI adoption away from basic chatbots towards more sophisticated, action-oriented systems. These agents leverage tools and APIs to streamline processes, highlighting the move towards practical AI applications that directly impact business operations.
Companies like Diliko are addressing the challenges of integrating AI, particularly for mid-sized organizations with limited resources. Diliko's platform focuses on automating data integration, organization, and governance through agentic AI, aiming to reduce manual maintenance and re-engineering efforts. This allows teams to focus on leveraging data for decision-making rather than grappling with infrastructure complexities. The Model Context Protocol (MCP) is a new standard developed by Dust AI that enables this level of automation, allowing AI agents to take concrete actions across business applications such as creating GitHub issues, scheduling calendar meetings, updating customer records, and even pushing code reviews, all while maintaining enterprise-grade security. Agentic AI is also making significant inroads into risk and compliance, as showcased by Lyzr, whose modular AI agents are deployed to automate regulatory and risk-related workflows. These agents facilitate real-time monitoring, policy mapping, anomaly detection, fraud identification, and regulatory reporting, offering scalable precision and continuous assurance. For example, a Data Ingestion Agent extracts insights from various sources, which are then processed by a Policy Mapping Agent to classify inputs against enterprise policies. This automation reduces manual errors, lowers compliance costs, and accelerates audits, demonstrating the potential of AI to transform traditionally labor-intensive areas. Recommended read:
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@shellypalmer.com
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Lyzr AI
, AI Accelerator Institute
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AI agents are rapidly transforming workflows and development environments, with new tools and platforms emerging to simplify their creation and deployment. Lyzr Agent Studio, integrated with Amazon's Nova models, allows enterprises to build custom AI agents tailored for specific tasks. These agents can be optimized for speed, accuracy, and cost, and deployed securely within the AWS ecosystem. The use of these AI agents are designed to automate tasks, enhance productivity, and provide personalized experiences, streamlining operations across various industries.
Google's Android 16 is built for "agentic AI experiences" throughout the platform, providing developers with tools like Agent Mode and Journeys. These features enable AI agents to perform complex, multi-step tasks and test applications using natural language. The platform also offers improvements like Notification Intelligence and Enhanced Photo Integration, allowing agents to interact with other applications and access photos contextually. This provides a foundation for automation across apps, making the phone a more intelligent coordinator. Phoenix.new has launched remote agent-powered Dev Environments for Elixir, enabling large language models to control Elixir development environments. This development, along with the ongoing efforts to create custom AI agents, highlight the growing interest in AI's potential to automate tasks and enhance productivity. As AI agents become more sophisticated, they will likely play an increasingly important role in various aspects of work and daily life. Flo Crivello, CEO of AI agent platform Lindy, provides a candid deep dive into the current state of AI agents, cutting through hype to reveal what's actually working in production versus what remains challenging. Recommended read:
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@siliconangle.com
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Gradient Flow
, SiliconANGLE
Thread AI Inc., a startup specializing in AI-powered workflow automation, has secured $20 million in Series A funding. The investment round was spearheaded by Greycroft, with significant contributions from Scale Venture Partners, Plug-and-Play, Meritech Capital, and Homebrew. Index Ventures, a major investor from the company's previous funding round, also participated. Founded last year by former Palantir Technologies Inc. employees Mayada Gonimah and Angela McNeal, Thread AI offers a platform called Lemma that simplifies the automation of complex, multi-step tasks, such as identifying the root causes of equipment failures.
The Lemma platform features a drag-and-drop interface that allows users to construct automation workflows from pre-packaged software components. This user-friendly design aims to provide a more accessible alternative to existing automation technologies, which can be cumbersome and require extensive technical expertise. According to McNeal, Thread AI addresses the common dilemma businesses face when implementing AI: choosing between rigid, prebuilt applications that don't fit their specific needs or investing heavily in custom AI workflow development. Thread AI's platform is built upon Serverless Workflow (SWF), an open-source programming language designed for creating task automation workflows. The company has enhanced SWF with additional features, making it easier to integrate AI models into automation processes. These AI models can also leverage external applications, such as databases, to handle user requests. A practical application of Lemma is troubleshooting hardware malfunctions. For instance, a manufacturer could create a workflow to collect data from equipment sensors, identify error alerts, and use AI to attempt to resolve the issue automatically. If the problem persists, the system can notify technicians. The platform also incorporates cybersecurity measures, including enhanced authentication features and an automatic vulnerability scanning mechanism. Recommended read:
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Arooj Ejaz@CustomGPT
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learn.aisingapore.org
, AI Accelerator Institute
Custom AI agents are transforming how organizations leverage data and automate tasks, enabling domain-specific responses and actions that enhance user interactions. These agents, tailored to specific organizational needs, provide intuitive and effective access to data and streamline various processes. This empowers businesses to improve their products and services by delivering more relevant and accurate information. The deployment of custom AI agents is particularly valuable for organizations seeking to streamline operations and enhance customer experiences.
White label AI software plays a crucial role in this evolution, enabling businesses to differentiate themselves and offer cutting-edge solutions. Companies can leverage robust AI platforms without the need for in-house development, allowing them to focus on growth strategies and market positioning. These platforms offer scalable architectures and customizable features that adapt to evolving market demands. By reselling white label AI solutions, businesses can open new revenue streams and solidify their market position, focusing on branding and customer engagement rather than infrastructure development. In 2025, the demand for white label AI software is expected to increase as more businesses recognize the benefits of leveraging pre-built solutions. These solutions provide access to advanced machine learning models, natural language processing, and analytics dashboards. With flexible APIs and modular components, integration into existing workflows becomes straightforward, allowing businesses to tailor solutions to their specific requirements. The key is to choose platforms that offer continuous updates and support, ensuring offerings remain competitive and meet the evolving needs of the market. Recommended read:
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@orases.com
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AI agents are rapidly transforming industries by automating tasks and enhancing decision-making, moving beyond simple automation to intelligent autonomy. These agents are being implemented across various sectors, promising significant improvements in efficiency and productivity. A strategic roadmap is essential for successful AI agent implementation, aligning technology with workflows and business objectives to ensure that these systems have a real impact on operations and decision-making. Without a clear structure, companies risk wasting investments on generic tools and isolated pilot projects.
The impact of AI agents is particularly evident in customer experience (CX), with companies increasingly integrating AI agents into their technology interactions. Cisco's recent Agentic AI Report highlights the transformative impact of these autonomous agents, which can retain memory, reason about tasks, and autonomously select actions to optimize outcomes with minimal human intervention. Cisco's data anticipates that enterprises expect 56% of their interactions with technology partners will be managed by AI agents within the next 12 months, increasing to 68% over three years. This accelerated adoption necessitates that vendors rapidly develop and deploy scalable, robust agentic AI solutions. Thomson Reuters is also leveraging this trend with agentic AI capabilities in its CoCounsel assistant, enabling autonomous, multi-step task execution in tax and accounting workflows. Early results show that processes like tax jurisdiction reviews have been drastically reduced from half a week to under an hour. The company plans to extend agentic AI to legal, risk, and compliance domains, connecting firm knowledge, codes, and internal documents into one workspace where AI handles complete workflows, rather than individual queries. This integration allows professionals to focus on higher-level tasks, ensuring that human expertise guides judgment and validates outputs. Recommended read:
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@www.marktechpost.com
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Meta is undergoing significant changes within its AI division, aiming to accelerate development and integrate AI more deeply into its advertising platform. The company is restructuring its AI organization into two teams: one focused on AI products and the other on advancing Artificial General Intelligence (AGI) research, particularly for its Llama models. This reorganization comes amidst a substantial talent exodus, with a significant portion of the original Llama research team having departed, many joining competitors like Mistral AI. Despite these challenges, Meta AI has reached a milestone of 1 billion monthly active users across Facebook, Instagram, WhatsApp, and Messenger, highlighting the broad reach of its AI initiatives.
Meta's focus is now shifting towards monetizing its AI capabilities, particularly through advertising. By the end of 2026, Meta intends to enable advertisers to fully create and target campaigns using AI, potentially disrupting traditional advertising agencies. Advertisers will be able to provide a product image and budget, and Meta's AI would generate the entire ad, including imagery, video, and text, while also targeting specific user demographics. This move aims to attract more advertisers, especially small and mid-sized businesses, by simplifying the ad creation process and leveraging Meta's extensive user data for targeted campaigns. However, Meta's increased reliance on AI raises concerns regarding data privacy and ethical considerations. The company has begun using data from Facebook and Instagram users, including posts, photos, and interactions with Meta AI, to train its AI models. Furthermore, Meta is reportedly planning to automate up to 90% of its risk assessments across Facebook and Instagram, including product development and rule changes. This shift raises questions about potential oversights and the impact on user safety, given the reliance on AI to evaluate potential risks and enforce policies. Recommended read:
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@www.marktechpost.com
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, AI News | VentureBeat
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Advancements in AI are rapidly shifting towards multi-agent systems, where specialized AI agents collaborate to perform complex tasks. These agents, envisioned as a team of expert colleagues, are designed to analyze data, interact with customers, and manage logistics, among other functions. The challenge lies in orchestrating these independent agents to work together seamlessly, ensuring they can coordinate interactions, manage shared knowledge, and handle potential failures effectively. Solid architectural blueprints are crucial for building reliable and scalable multi-agent systems, emphasizing the need for patterns designed for reliability and scale from the outset.
LangGraph Platform is emerging as a key tool for deploying these complex, long-running, and stateful AI agents. It addresses challenges such as maintaining open connections for extended processing times, preventing timeouts, and recovering from exceptions. The platform supports launching agent runs in the background, provides polling and streaming endpoints to monitor run status, and implements strategies to minimize exceptions. Features like heartbeat signals, configurable retries, and multiple streaming modes are crucial for reliable agent operation, providing end-users with intermediate output to demonstrate progress during lengthy processes. A new paradigm called Group Think is being explored to further enhance the efficiency of multi-agent reasoning. This approach allows multiple reasoning agents within a single LLM to operate concurrently, observing each other's partial outputs at the token level. By enabling real-time mutual adaptation among agents mid-generation, Group Think reduces duplication and speeds up collaborative LLM inference. This contrasts with traditional sequential or independently parallel sampling techniques, which often introduce delays and limit the practicality of deploying multi-agent LLMs in time-sensitive or computationally constrained environments. Recommended read:
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@www.microsoft.com
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www.microsoft.com
Microsoft is introducing Magentic-UI, an open-source research prototype designed as a human-centered AI agent. This experimental tool is built to assist users in completing complex, web-based tasks in real time, directly within a web browser. Unlike fully autonomous systems, Magentic-UI emphasizes a transparent and controllable experience. The platform is geared towards tasks that are action-oriented and extend beyond simple web searches, providing a unique approach to human-AI collaboration on the web.
Magentic-UI builds upon Magentic-One and is powered by AutoGen, Microsoft's agent framework. It is available under the MIT license and on Azure AI Foundry Labs, offering developers, startups, and enterprises a space to explore Microsoft Research innovations. The system is integrated with Azure AI Foundry models and agents, with code samples available for those looking to integrate Azure AI agents into Magentic-UI's multi-agent architecture. Magentic-UI is capable of tasks involving web browsing, Python and shell code execution, and file understanding. Key features include collaborative planning, where users can modify the agent's plan directly through a plan editor or textual feedback. It also supports collaborative execution, allowing users to pause the system, provide natural language feedback, or directly control the browser to guide the AI agent, fostering a seamless blend of human and artificial intelligence. Recommended read:
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@blogs.microsoft.com
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Microsoft Build 2025 showcased the company's vision for the future of AI with a focus on AI agents and the agentic web. The event highlighted new advancements and tools aimed at empowering developers to build the next generation of AI-driven applications. Microsoft introduced Microsoft Entra Agent ID, designed to extend industry-leading identity management and access capabilities to AI agents, providing a secure foundation for AI agents in enterprise environments using zero-trust principles.
The announcements at Microsoft Build 2025 demonstrate Microsoft's commitment to making AI agents more practical and secure for enterprise use. A key advancement is the introduction of multi-agent systems within Copilot Studio, enabling AI agents to collaborate on complex business tasks. This system allows agents to delegate tasks to each other, streamlining processes such as sales data retrieval, proposal drafting, and follow-up scheduling. The integration of Microsoft 365, Azure AI Agents Service, and Azure Fabric further enhances these capabilities, addressing limitations that have previously hindered the broader adoption of agent technology in business settings. Furthermore, Microsoft is emphasizing interoperability and user-friendly AI interaction. Support for the agent-to-agent protocol announced by Google could enable cross-platform agent communication. The "computer use" feature for Copilot Studio agents allows them to interact with desktop applications and websites by directly controlling user interfaces, even without API dependencies. This feature enhances the functionality of AI agents by enabling them to perform tasks that require interaction with existing software and systems, regardless of API availability. 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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@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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Amicie Ourega@Yseop
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AI agents are rapidly emerging as the next major advancement beyond chatbots, offering the potential to revolutionize how businesses operate and individuals live. These agents are distinguished by their capacity for autonomous action, enabling them to execute tasks and make decisions independently. Microsoft is expanding its suite of AI agents for ERP systems, with several new agents soon available for public preview in Dynamics 365. These agents promise to streamline business processes across various functions, including finance, supply chain, and operations, marking a shift towards AI-first operations.
The key difference between AI-powered assistants and autonomous agents lies in their roles. While assistants primarily support human tasks, agents function as "digital colleagues," taking on specific tasks and responsibilities. In ERP systems, agents can automate high-volume, rules-based activities, reducing manual effort, improving accuracy, and accelerating decision-making. Microsoft highlights the potential of these agents to transform how business processes are orchestrated and executed, paving the way for intelligent and scalable automation. DataRobot has also launched its federal AI application suite, designed specifically for government agencies. This suite includes a range of agents and custom applications intended to enhance efficiency and impact within high-security environments. The Account Reconciliation Agent for example, accelerates the period-end close by matching ledger entries, flagging discrepancies, and recommending resolution steps. These advancements underscore the growing recognition of AI agents as a powerful tool for driving innovation and improving performance across diverse industries. Recommended read:
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@www.datarobot.com
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DataRobot has recently unveiled its Federal AI Application Suite, a collection of AI agents and custom applications tailored for government agencies. The suite is designed to facilitate the delivery of mission-critical AI solutions within secure environments, enabling government entities to leverage AI for enhanced efficiency and impact. This launch underscores the growing importance of AI in the public sector and DataRobot's commitment to providing purpose-built solutions that meet the unique needs of government agencies. The suite promises to streamline operations and improve decision-making processes by automating tasks and providing intelligent insights.
Microsoft is embracing open protocols with Agent2Agent (A2A), enabling agents to collaborate across clouds, platforms, and organizational boundaries. With Microsoft Copilot acting as the "UI for AI", A2A can enable structured agent communication—exchanging goals, managing state, invoking actions, and returning results securely and observably. Developers can use tools they know, like Semantic Kernel or LangChain, and still interoperate. Every call travels through enterprise-grade safeguards: Microsoft Entra, mutual TLS, Azure AI Content Safety, and full audit logs. Furthermore, the integration of AI agents into Enterprise Resource Planning (ERP) systems signals a new era in business process automation. Microsoft Dynamics 365 is at the forefront of this shift, introducing agents that redefine how finance, supply chain, and operations teams manage their workflows. These agents, acting as "digital colleagues," automate high-volume, rules-based activities, reducing manual effort, improving accuracy, and accelerating decision-making. This move towards AI-first operations promises to transform industries by making ERP systems more intelligent, cost-effective, and scalable. Recommended read:
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@www.microsoft.com
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The business world is on the cusp of a significant transformation as AI agents emerge as powerful tools for automating and streamlining processes. Microsoft Dynamics 365 is leading the charge by introducing new ERP agents for public preview, designed to redefine how finance, supply chain, and operations teams manage their work. These agents represent a shift towards AI-first operations, promising to reduce manual effort, improve accuracy, and accelerate decision-making across various business functions. As organizations increasingly integrate AI into their strategies, the focus is shifting from the hype surrounding AI to its practical applications in driving tangible business value.
Microsoft's new ERP agents function as "digital colleagues," taking on specific tasks and automating workflows. Unlike AI-powered assistants that merely support human actions, these autonomous agents can execute entire processes, such as lead generation, order management, and account reconciliation, with minimal human intervention. These agents excel in ERP systems where high-volume, rules-based activities are common, streamlining complex processes like source-to-pay and project-to-profit. The Account Reconciliation Agent, for instance, can accelerate the period-end close by matching ledger entries, flagging discrepancies, and recommending resolution steps, freeing up professionals to focus on more strategic tasks. Beyond ERP, AI agents are making inroads into go-to-market (GTM) teams, redefining roles in prospecting, forecasting, and customer success. Rather than being just "glorified chatbots," these agents are goal-oriented systems that observe, decide, and act within defined environments, making intelligent decisions to scale existing successful strategies. Companies like SAS are also developing AI agents with built-in guardrails, combining traditional rule-based analytics with machine learning to ensure controlled and predictable automation. IBM and Oracle are also joining the party with watsonx Orchestrate, a drag-and-drop interface for building AI agents for deployment in the Oracle Cloud Infrastructure (OCI). The AI revolution is not just about replacing human workers but about augmenting their capabilities and driving efficiency across the enterprise. Recommended read:
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Evan Ackerman@IEEE Spectrum
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Amazon has unveiled Vulcan, an AI-powered robot with a sense of touch, designed for use in its fulfillment centers. This groundbreaking robot represents a "fundamental leap forward in robotics," according to Amazon's director of applied science, Aaron Parness. Vulcan is equipped with sensors that allow it to "feel" the objects it is handling, enabling capabilities previously unattainable for Amazon robots. This sense of touch allows Vulcan to manipulate objects with greater dexterity and avoid damaging them or other items nearby.
Vulcan operates using "end of arm tooling" that includes force feedback sensors. These sensors enable the robot to understand how hard it is pushing or holding an object, ensuring it remains below the damage threshold. Amazon says that Vulcan can easily manipulate objects to make room for whatever it’s stowing, because it knows when it makes contact and how much force it’s applying. Vulcan helps to bridge the gap between humans and robots, bringing greater dexterity to the devices. The introduction of Vulcan addresses a significant challenge in Amazon's fulfillment centers, where the company handles a vast number of stock-keeping units (SKUs). While robots already play a crucial role in completing 75% of Amazon orders, Vulcan fills the ability gap of previous generations of robots. According to Amazon, one business per second is adopting AI, and Vulcan demonstrates the potential for AI and robotics to revolutionize warehouse operations. Amazon did not specify how many jobs the Vulcan model may create or displace. Recommended read:
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@www.aiwire.net
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The rise of AI agents is rapidly transforming the business landscape, with companies like IBM and Oracle leading the charge in integrating these intelligent tools into the workforce. IBM kicked off its annual Think conference in Boston, highlighting generative AI and agentic AI tools as central themes. CEO Arvind Krishna noted the expectation of a billion new applications being built using generative AI, emphasizing the need to address the challenges of AI deployment, execution, and return on investment. IBM is touting its watsonx enterprise AI platform and rolling out new features, many designed to tame the AI’s deployment, execution, and ROI issues.
IBM and Oracle are expanding their partnership to bring IBM's watsonx, a portfolio of AI products, to Oracle Cloud Infrastructure (OCI). This collaboration aims to create a new era of multi-agentic, AI-driven productivity and efficiency across enterprises. Greg Pavlik, executive vice president at Oracle Cloud Infrastructure, emphasized the importance of seamless AI integration across businesses, stating that the expanded partnership will provide customers with new ways to transform their operations using AI. IBM is making its watsonx Orchestrate AI agent offerings available on OCI in July. Furthermore, the integration of AI agents is expected to significantly impact human resources. Salesforce research indicates that HR leaders are planning to redeploy a quarter of their workforce to focus on agentic AI-related tasks, as AI agent adoption is projected to grow by 327% by 2027. This shift highlights the increasing importance of digital labor and the need for reskilling employees to adapt to the changing demands of the modern workforce. 81% of HR chiefs plan to reskill their employees for better job opportunities in the digital labor era. Recommended read:
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