News from the AI & ML world
DeeperML - #automation
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@orases.com
// 7d
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
// 8d
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
// 16d
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LangChain Blog
, 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
// 18d
References:
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
// 22d
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
// 23d
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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
// 27d
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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
// 29d
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
// 29d
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
// 30d
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
// 33d
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
// 33d
References:
insideAI News
, www.aiwire.net
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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Noor Al-Sibai@futurism.com
// 42d
Duolingo is making a significant shift to an AI-first model, restructuring its operations to focus on generative AI for content creation and process automation. CEO Luis von Ahn announced plans to gradually reduce the company's reliance on contractors, aiming to automate tasks wherever possible. This transition marks a fundamental cultural shift, with leadership emphasizing the transformative power of AI in reshaping how work is accomplished. This mirrors the company's early adoption of a "mobile-first" strategy in 2012 which led to significant recognition.
This strategic move is driven by the need to deliver app content more quickly and at a greater scale. Duolingo states that manual content creation is no longer viable for meeting the company's needs. Replacing slow, manual processes with AI-driven solutions allows for the faster provision of content for learners. The company reported that AI has enabled them to build more courses in one year than in the previous twelve years combined. A large content expansion was recently launched by the company, releasing 148 new language courses which were all created using generative AI. The implementation of AI extends beyond content creation, with plans to integrate it into hiring processes and employee performance reviews. Teams will be encouraged to prioritize automation before requesting additional resources. CEO Luis von Ahn stated that the changes are not intended to reduce the company's focus on employee well-being, adding that the move is not about replacing employees with AI but removing bottlenecks. Instead, the goal is to empower employees to focus on creativity, accelerating Duolingo's mission to deliver language instruction globally. Recommended read:
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@Salesforce
// 43d
References:
Salesforce
, Salesforce
Agentic AI is rapidly transforming various sectors, from government operations to small businesses. Salesforce executives highlight the potential of agentic AI to assist overstretched government workers by automating routine tasks and improving efficiency. The focus is shifting from automating basic tasks to creating intelligent systems that adapt and learn, providing personalized and efficient support. This evolution promises to reshape how work is done, streamlining processes and enhancing productivity.
Companies are quickly adopting AI agents to enhance customer support and streamline operations, leading to a new competitive landscape. Microsoft has launched powerful AI agents designed to transform the workday and challenge Google’s workplace dominance. These agents, such as the 'Researcher' and 'Analyst' agents, are powered by OpenAI’s deep reasoning models and can handle complex tasks, such as research and data analysis, that previously required specialized human expertise. This increased productivity across sectors signifies a major shift in how businesses operate. Dynamics 365 Customer Service now offers three AI service agents in public preview: Case Management, Customer Intent, and Customer Knowledge Management agents. These agents learn to address emerging issues, uncover new knowledge, and automate manual processes to boost business efficiency and reduce costs. The Case Management Agent automates key tasks throughout the lifecycle of a case, while the Customer Intent Agent uses generative AI to analyze past interactions and provide tailored solutions. This represents a significant step towards autonomous systems that improve customer experiences and reduce the burden on human agents. Recommended read:
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@orases.com
// 44d
References:
orases.com
, John Werner
AI agents are rapidly evolving from simple digital assistants to sophisticated systems that are transforming workflows across various organizations and industries. These agents are now capable of actively managing data, automating decision-making processes, and driving cross-functional workflows, marking a significant shift in how businesses operate. This evolution presents both opportunities and challenges, particularly concerning fragmentation, security, and the need for scalable tools to effectively manage these AI agents. Companies are racing to adopt these technologies to gain a competitive edge.
The adoption of AI agents is particularly impactful for Small and Mid-sized Businesses (SMBs), which often lack the resources of larger enterprises. AI agents offer SMBs a new competitive edge by streamlining operations and improving productivity without requiring extensive manpower or complex processes. Intelligent automation, driven by AI, is replacing manual tasks with smarter workflows, freeing up employees to focus on strategic initiatives. This trend is evident across various sectors, as companies explore how to best leverage AI agents to optimize their operations and communication. One innovative solution is the SOP-driven LLM agent framework, which integrates Standard Operating Procedures (SOPs) to ensure reliable execution and enhance productivity. This framework addresses common challenges faced by Large Language Models (LLMs), such as inaccuracies and inconsistencies, by structuring SOPs as a tree, allowing for intuitive workflow creation. Initial evaluations demonstrate remarkable accuracy, exceeding 99.8% in real-world scenarios, with AI-powered solutions being developed up to 10 times faster than traditional methods. The framework is transforming enterprise operations and exploring industry applications in engineering, data analytics, and data science, heralding a new era of AI-driven automation. Recommended read:
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@www.marktechpost.com
// 47d
References:
www.marktechpost.com
, TestingCatalog
The rise of AI agents is transforming industries, enabling systems to perform complex tasks with minimal human intervention. This shift is powered by advancements in Agent Development Kits (ADKs) like Google's new open-source Python framework, streamlining agent creation and deployment. Emerging roles like Agent Architects are becoming increasingly important, focusing on designing and implementing AI agent workflows. These architects bridge the gap between business goals and intelligent automation, mapping processes to agent workflows using low-code tools. Lyzr AI, for example, highlights the growing demand for Agent Architects, predicting it to be one of the next 100,000 jobs, emphasizing the need for individuals who understand both AI and how to turn processes into intelligent agent workflows.
The Sequence Engineering article highlights Google's new Agent Development Kit (ADK) as a key enabler for multi-agent systems. The ADK is designed with composability and extensibility in mind, it empowers researchers and developers to build robust agentic systems ranging from simple task handlers to complex, multi-agent orchestration layers. Google's Gemini is also incorporating AI agents, with tests revealing a new "Search" agent within Gemini's prompt composer. This "Search" agent could provide quicker access to Google Search’s full power, implying an expanded role for retrieval-augmented responses which streamline access to advanced capabilities. Citibank's recent report underscores the transformative potential of agentic AI within financial services. Agentic AI is capable of autonomous analysis and intelligent automation which can reshape everything from compliance and risk modeling to personalized advisory services. These agents will increasingly inhabit every layer of financial operations, from client-facing digital advisors to internal compliance monitors. The bank envisions agentic AI as a new operating system for finance, capable of initiating and managing actions, as opposed to simply generating content, leading to significant productivity gains and a "Do It For Me" economy. Recommended read:
References :
John Werner,@John Werner
// 48d
References:
John Werner
Companies are rapidly adopting AI agents to enhance various business operations. Salesforce, for example, has integrated AI agents into their Agentforce platform, amassing insights from over 500,000 customer conversations, showcasing how AI can drive both empathy and efficiency. The interest in leveraging AI agent designs to optimize processes is growing, as businesses seek to harness the power of intelligent automation. Companies are increasingly interested in how to get the most out of new agentic AI designs.
Agent Architect is emerging as a critical role, tasked with designing and implementing intelligent agent workflows using low-code tools. These architects bridge the gap between business goals and AI automation, mapping processes to workflows and designing agent behaviors that adapt and evolve with minimal human intervention. Lyzr is helping to train Agent Architects with the aim of accelerating automation strategies. This new role is essential, as analysts, developers, and system engineers were not originally hired to manage agentic AI. Furthermore, partnerships are forming to extend the reach of AI agents into government sectors. Leidos and Moveworks are collaborating to provide agentic AI solutions to government agencies in the U.S., U.K., and Australia, aiming to improve efficiency for government workers. Moveworks has received security certifications from Leidos, showing their capacity to support government agencies with secure AI solutions. Additionally, Zero Networks is promoting automated microsegmentation to enhance cybersecurity through zero trust policies, isolating assets within networks to limit the impact of cyberattacks, with automation seen as key to practical real-world network security. Recommended read:
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@www.helpnetsecurity.com
// 49d
References:
hackread.com
, Help Net Security
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StrikeReady has launched its next-generation Security Command Center v2, an AI-powered platform designed to help security teams move beyond basic alert processing and automate threat response. For years, security teams have struggled with siloed tools, fragmented intelligence, and a constant stream of alerts, forcing them to operate in a reactive mode. Traditional Security Operations platforms, meant to unify data and streamline response, often added complexity through customization and manual oversight. The new platform aims to address these challenges by bringing automated response to assets, identities, vulnerabilities, and alerts.
The Security Command Center v2 offers several key business outcomes and metrics. These include proactive risk visibility with a consolidated risk view across identities, assets, and vulnerabilities, validated in a single command center interface. This is intended to enable informed, strategic planning instead of constant firefighting. The platform also offers radical time reduction, with risk validation using threat intelligence dropping from hours to minutes and alert processing reduced from an hour to just one minute, freeing analysts for threat hunting. All alerts, regardless of severity, are processed at machine speed and accuracy. According to Alex Lanstein, CTO at StrikeReady, the goal is to help security teams "escape the cycle of perpetual reactivity." With this platform, organizations can control and reduce risk in real-time, closing security gaps before they're exploited. Furthermore, the new platform offers better, faster, and more cost-effective deployments, with automated workflows and capabilities going live in as little as 60 minutes. Lower operational expenses are also expected, with examples such as phishing alert backlogs cleared in minutes, reducing manual efforts and potentially saving over $180,000 annually. The platform includes native case management, collaboration, and real-time validation, streamlining security operations and minimizing reliance on external ticketing systems. Recommended read:
References :
@www.developer-tech.com
// 52d
AI agents are rapidly evolving from experimental tools to integral components of enterprise environments, automating complex tasks and redefining online interactions. However, despite the intense interest, many professionals express frustration over the gap between the potential of AI agents and their limited presence in live enterprise settings. This skepticism is justified by the systemic failure modes observed in multi-agent systems, highlighting the challenge of translating agent potential into reliable performance. A key issue is the ambiguous definition of "agent," with companies using the term loosely to describe everything from basic chatbots to sophisticated autonomous systems.
What technologists truly envision is an autonomous software system capable of perceiving its environment, reasoning through complex problems, and taking independent actions to achieve defined goals. These agents exhibit genuine autonomy, adapt to changing circumstances, maintain context across interactions, and proactively pursue objectives rather than merely responding to queries. Real-world implementations are already emerging, such as "deep research" tools that autonomously conduct sophisticated investigations by breaking down queries, gathering and analyzing diverse sources, and dynamically adjusting their approach. These tools offer a compelling glimpse of what mature agents could accomplish across broader domains. Infrastructure is now being rebuilt to accommodate AI agents. Systems like CAPTCHAs, credit card verification, and authentication protocols, which were originally designed for human actors, are now cracking under the pressure of automation. This transition will unlock entirely new possibilities, allowing AI agents to perform tasks that humans find too tedious or time-consuming. For example, agents can granularly optimize privacy preferences across thousands of sites, compare prices across hundreds of retailers in seconds, and maintain context across multiple interactions, streamlining processes and enhancing efficiency in a way that was impractical in a human-centric web. Recommended read:
References :
Harshit Kandoi@Towards AI
// 52d
AI agents are rapidly gaining traction across various industries, transforming how businesses operate and manage workflows. These autonomous software systems are designed to perceive their environment, reason through complex problems, and take independent actions to achieve defined goals. Unlike traditional AI, which simply executes predetermined instructions, these agents exhibit genuine autonomy, adapt to changing circumstances, maintain context, and employ multi-step reasoning, effectively proactively pursuing objectives rather than just responding to queries. From small businesses to large organizations, AI agents are being implemented to automate repetitive tasks and handle increasingly complex processes, demonstrating their versatility and potential impact.
The integration of AI agents is streamlining tasks and opening up new possibilities across sectors. In the realm of data observability, companies like Monte Carlo are introducing AI agents to automate challenging tasks for data engineers, such as developing data observability monitors and pinpointing the root causes of data pipeline problems. Moreover, platforms like Zoho Cliq are incorporating AI agents as smart assistants to manage routine tasks, freeing up human employees for more productive work. These agents can check availability, schedule meetings, share agendas, and send reminders, ultimately increasing efficiency and reducing errors. However, the rise of AI agents also necessitates a rethinking of established digital infrastructure. Traditional systems built for human actors, such as CAPTCHAs and authentication protocols, are beginning to show strain under the pressure of automation. This shift calls for new protocols and mechanisms, like Agent Passports, to ensure trust, authentication, and security in online interactions. As AI agents become more prevalent, adapting the web infrastructure to accommodate their autonomous interactions will be crucial for unlocking their full potential and creating a more seamless and efficient digital world. Recommended read:
References :
@www.analyticsvidhya.com
// 59d
AI agents are rapidly transforming business operations, moving beyond simple automation to enabling AI-driven reinvention. A recent McKinsey study highlights a shift where businesses need to look beyond merely making existing workflows more efficient. Instead, the focus is on replacing outdated workflows and creating entirely new capabilities through autonomous AI agents. These agents can act independently, make decisions, and adapt dynamically, fundamentally reshaping how organizations operate by proactively managing tasks and navigating complex IT environments. This marks a significant step toward achieving new levels of productivity and innovation.
Leveraging conversational AI for customer service is a key area of transformation, as AI agents are being used to provide exceptional, efficient, and personalized support. Customers can experience instant, helpful interactions, bypassing the traditional wait times associated with human agents. The conversational AI market is projected to grow significantly, reflecting the increasing adoption of AI chatbots, virtual agents, and agent-assist solutions. These technologies are not only reducing operational costs but also elevating brand reputation through improved customer experiences. Google's launch of the Agent2Agent (A2A) protocol represents a pivotal moment in fostering collaboration between AI agents across diverse platforms. This open interoperability protocol aims to standardize communication between agents, enabling them to securely exchange information, coordinate actions, and integrate across enterprise systems. Supported by over 50 technology partners, A2A addresses the challenges of siloed systems, paving the way for automating complex workflows and enhancing overall productivity. By releasing A2A as open source, Google invites the broader community to contribute and further refine the functionality, fostering innovation and scalability in the agentic AI landscape. Recommended read:
References :
Bernard Marr,@Bernard Marr
// 63d
References:
Source Asia
, Bernard Marr
The rapid advancement of Artificial Intelligence is poised to transform the job market significantly, with projections indicating that AI could impact up to 70% of jobs by 2030. This "Great Skill Shift" signifies a move towards a skills-based, human-centered ecosystem. Rather than simply automating tasks, AI is fundamentally reshaping the labor landscape, requiring individuals to adapt and acquire new skills to remain relevant in the evolving workplace. Microsoft envisions a future where AI acts as a companion, built around individual needs and expectations, highlighting the importance of personalization in this technological revolution.
As Microsoft celebrates 50 years of innovation, its contributions to AI development are becoming ever more clear. From the initial launch of its operating system in 1985 to Bill Gates's prediction about the internet, Microsoft has continued to adapt to changing technologies. Foundational papers by Microsoft Research, like those on Bayesian Networks and the Z3 theorem prover, set the stage for current advancements in AI. Microsoft has been committed to building a trusted AI platform, including tools, that will be used in the future. Microsoft's journey in AI is marked by key milestones, including the launch of Bing with natural language capabilities in 2009. Bing introduced machine learning features that provided search suggestions and related searches. The 2015 launch of Project Oxford, now Azure AI Foundry, gave developers intelligent technologies for building smarter apps featuring face detection, voice recognition, and language understanding. These achievements highlight Microsoft's dedication to developing AI tools and resources, providing developers with the ability to create AI-powered applications. Recommended read:
References :
@upwarddynamism.com
// 66d
The rise of AI agents is rapidly transforming various sectors, from online retail to enterprise applications. These intelligent software entities are designed to operate autonomously, achieving specific goals by formulating strategies, executing them, and adapting to changing circumstances. Companies are investing heavily in AI agents to automate tasks, streamline workflows, and unlock productivity gains, leading to a significant shift in how businesses operate and engage with customers. Experts predict that AI agents will soon augment a vast number of jobs, automating tasks and enhancing decision-making processes across industries.
AI agents are already making a significant impact on online shopping. Retailers are tapping into AI agents to deepen customer engagement, enhance offerings, and maintain a competitive edge. By leveraging customer data and generative AI tools, these agents provide personalized recommendations, enriching product catalogs with detailed information and offering omnichannel support. AI agents can act as virtual assistants, providing tailored product recommendations and boosting conversion rates, ultimately enhancing customer satisfaction. In the enterprise sector, AI agents are evolving from simple assistants to independent entities capable of perceiving, evaluating, and acting upon data. IDC estimates that over 50% of the enterprise application market is already AI assistant or AI advisor-enhanced, with 20% further supplemented by complete AI agents. Over the next few years, advancements in generative and agentic AI will push enterprise applications towards agent-led models, where agents replace entire functional areas. Eventually, companies may enlist entire fleets of AI agents to manage supply chains, customer relations, and other critical functions, signaling a major shift in the way businesses utilize software. Recommended read:
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Nishant N@MarkTechPost
// 68d
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AI News
, MarkTechPost
Amazon has launched Nova Act, an advanced AI model and experimental developer kit (SDK) designed to create smarter agents capable of executing tasks within web browsers. This initiative aims to move beyond simple query responses and information retrieval, envisioning agents that can perform tangible, multi-step tasks in digital environments without constant human supervision. The SDK allows developers to build agents that automate web tasks like submitting out-of-office notifications, scheduling calendar holds, and enabling automatic email replies.
The Amazon Nova Act SDK helps developers break down complex workflows into reliable "atomic commands," enabling interactions with specific interface elements like dropdowns and popups. Detailed instructions can be added to refine these commands, allowing developers to instruct an agent to bypass insurance upsells, for example, during checkout. Amazon emphasizes Nova Act's exceptional performance on benchmarks, highlighting its impressive scores on internal evaluations and its ability to prioritize reliability when completing complex tasks, positioning it as a challenge to existing platforms like OpenAI's Agents SDK. Recommended read:
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