Latest news with #autonomousagents
Associated Press
2 days ago
- Business
- Associated Press
Strategic AI Advisors Unveils RemoteAgent Breakthrough AI Platform
Strategic AI Advisors announces RemoteAgent, an AI agent platform that revolutionizes business intelligence by autonomously executing tasks for executives. United States, July 26, 2025 -- RemoteAgent: AI That Actually Gets Things Done Strategic AI Advisors announces RemoteAgent, a breakthrough AI agent platform that transforms how executives handle research, analysis, and business intelligence. Most AI tools are glorified chatbots. They answer questions when you ask them, but then you're left to figure out what to do with the responses. RemoteAgent changes that equation entirely; it's designed to execute tasks autonomously, not just provide information. Beyond Question-and-Answer: AI That Takes Action RemoteAgent represents a fundamental shift from reactive AI to proactive AI agents. Instead of waiting for prompts, RemoteAgent operates as an autonomous team member that handles research, synthesis, reporting, and repetitive workflows without constant supervision. The platform's capabilities become clear through real-world application. When asked to analyze Enterprise AI Solutions' ( competitive landscape, a task that typically requires hours of manual research and analysis, RemoteAgent delivered a comprehensive report. The report identified direct and indirect competitors, provided market positioning insights, offered revenue optimization strategies, and conducted a detailed market opportunity assessment. The analysis included specific pricing data, conversion timelines, and strategic recommendations that would typically require a consulting team weeks to compile. Executive Efficiency at Enterprise ScaleReal-Time Business Intelligence Without the Dashboards RemoteAgent eliminates the friction between executives and their data. Instead of logging into multiple BI tools or waiting for analyst reports, leaders can request key metrics, insights, and KPIs through natural language queries. The system connects directly to existing business systems, pulling real-time data and presenting it in immediately actionable formats. Custom-Fit Organizational Integration Unlike one-size-fits-all AI solutions, RemoteAgent is tailored to meet specific organizational needs. Companies define the AI agent's personality, responsibilities, data permissions, and access levels, creating what amounts to an elite analyst and operations manager who never forgets critical details or context. Zero Learning Curve Implementation RemoteAgent deploys within minutes and operates seamlessly within existing tools, including CRM systems, Slack, Notion, Google Workspace, and other business platforms. Teams don't need to learn new software or change established workflows. The AI agent integrates seamlessly into current operations while dramatically expanding analytical capabilities. Enterprise-Grade Security Meets Adaptive Intelligence Security concerns often derail AI adoption in enterprise environments. RemoteAgent addresses this through strict permission protocols and data governance designed for secure corporate environments. The platform operates under enterprise-grade security standards while maintaining the flexibility to access necessary business data. The system's adaptive intelligence sets it apart from static AI tools. RemoteAgent learns individual preferences and workflows across team members, delivering increasingly tailored support over time. This personalization ensures that insights and recommendations become more relevant and actionable as the system gains a deeper understanding of the organization's context. Proven Performance in Complex Analysis The competitive analysis example demonstrates RemoteAgent's sophisticated analytical capabilities. The system identified market positioning opportunities, calculated specific revenue potential (annual revenue of $13-32M within 5 years), and provided strategic recommendations, including tiered subscription models and global expansion strategies. This level of analysis traditionally requires specialized consulting expertise and significant time investment. RemoteAgent delivered comprehensive insights autonomously, showcasing the platform's ability to handle complex business intelligence tasks that go far beyond simple information retrieval. The Future of Executive Decision-Making RemoteAgent represents a clear evolution in how executives access and utilize business intelligence. By combining autonomous execution with enterprise-grade security and adaptive learning, the platform addresses the gap between AI potential and practical business application. For organizations ready to move beyond experimental AI implementations toward systems that deliver measurable productivity gains, RemoteAgent offers a proven approach to AI-augmented decision-making. Experience RemoteAgent's Analytical Power Firsthand See RemoteAgent's capabilities in action with a live demonstration. Try the same type of strategic analysis that delivered comprehensive competitive insights for Enterprise AI Solutions. Try it yourself: Visit RemoteAgent here and use this prompt:Research [INSERT COMPANY NAME] and present your research findings organized by the PAS framework, with each section containing: Real Examples: When we analyzed Apple using this framework, RemoteAgent delivered a comprehensive PAS analysis covering market position, strategic challenges, and actionable recommendations. Check it out. A more detailed strategic analysis of Apple showcased RemoteAgent's ability to provide executive-level insights with supporting data and implementation timelines. See the full report. Experience the difference between AI that answers questions and AI that delivers strategic intelligence you can immediately act upon. Strategic AI Advisors ( ) specializes in helping organizations implement AI solutions that deliver tangible business value. Contact us to learn how RemoteAgent can transform your team's analytical capabilities. About Strategic AI Advisors Strategic AI Advisors is a leading consultancy specializing in AI solutions for enterprises. The company helps organizations leverage AI to improve operational efficiency, drive data-driven decision-making, and transform business intelligence. Media Contact Cat Valverde Enterprise AI Solutions Email: [email protected] Website Contact Info: Name: Cat Valverde Email: Send Email Organization: Enterprise AI Solutions Website: Release ID: 89165692 If there are any problems, discrepancies, or queries related to the content presented in this press release, we kindly ask that you notify us immediately at [email protected] (it is important to note that this email is the authorized channel for such matters, sending multiple emails to multiple addresses does not necessarily help expedite your request). Our responsive team will be available round-the-clock to address your concerns within 8 hours and take necessary actions to rectify any identified issues or support you with press release takedowns. Ensuring accurate and trustworthy information is our unwavering commitment.
Yahoo
4 days ago
- Business
- Yahoo
viaNexus Unveils MCP Service to Enable Agent-Driven Access to Financial Data
NEW YORK, July 24, 2025 /PRNewswire/ -- viaNexus, the high-performance financial data platform built for intelligent systems, today announced the release of its MCP Service, a first-of-its-kind MCP implementation enabling a proper client/server architecture for autonomous agents to securely discover, request, and consume financial data — without manual Authentication/Authorization and paywall integration removinghuman intervention. The viaNexus MCP Service leverages the emerging Model Context Protocol (MCP) to deliver real-time, entitlement-aware access to market data, filings, fundamentals, news and more. Each agent is assigned a scoped identity and permission set, allowing precise control over what data can be accessed, by whom, and under what terms. "Agents are the new users, and data is their fuel," said Tim Baker, CEO of viaNexus. "With the MCP Service, agents can connect directly to our data platform — with secure authentication, entitlements, and governance built in from the start." Unlike current MCP server implementations, which are currently being advertised as API interfaces, often lack native support for secure authentication protocols, and agent-specific access controls, the viaNexus solution introduces several key innovations: Agent-Scoped Identity and Access Control — Unique, auditable access tied to organizational policies and account-level entitlements Asynchronous Authorization Workflow — Secure approval flows and timed bearer tokens remove reliance on human-mediated OAuth flows, which are incompatible with autonomous workflows Built-In Paywall Integration — Data providers can configure usage-based access and pricing, confident that monetization and compliance are enforced programmatically Native Integration with viaNexus Data — Agents can retrieve structured data on demand, fueling analysis, reasoning, or downstream processes "This is just the beginning," said Pedro Aguayo, CTO of viaNexus. "We're already working on open sourcing the entire client stack — including connectors, configuration tools, and telemetry. Our goal is to make secure, agentic data workflows easy to build, deploy, and scale." Read the blog post here: View the demo here: Stay up to date with future releases by signing up to our Newsletter here: viaNexus is also inviting select beta customers to test the viaNexus MCP service. Interested parties can reach the team at MCP-beta@ About viaNexusviaNexus is a next-generation financial data platform purpose-built for both data publishers and data consumers. From real-time prices to structured financial content, viaNexus delivers high-performance, entitlement-aware data access through APIs and next-gen protocols like MCP. The platform supports fintechs, institutions, and data providers looking to scale in an intelligent, compliant, and cost-effective way. Learn more at Media Contact: Tim Baker, View original content to download multimedia: SOURCE viaNexus Fehler beim Abrufen der Daten Melden Sie sich an, um Ihr Portfolio aufzurufen. Fehler beim Abrufen der Daten Fehler beim Abrufen der Daten Fehler beim Abrufen der Daten Fehler beim Abrufen der Daten
South China Morning Post
08-07-2025
- Business
- South China Morning Post
Alibaba expert envisions AI agents transforming daily life in 5 years
Digital colleagues will be a part of everyday life in the next five years, according to an artificial intelligence (AI) expert from Alibaba Group Holding's cloud computing unit, who envisions an ecosystem of application developers creating AI agents to cater to consumer and business demands. 'Agentic AI is very popular in the industry right now,' said Huang Fei, vice-president of Alibaba Cloud and head of the company's Tongyi Natural Language Processing Lab. Speaking at the China Conference 2025 organised by the Post on Tuesday, he was referring to systems that use AI to act autonomously on behalf of users to pursue goals or complete tasks. The future AI landscape would be dominated by a small number of fundamental model providers and a larger number of developers producing such agents, Huang said. His comments underscored Alibaba's strategy of becoming a key provider of AI infrastructure and fundamental models. The company's Qwen series of open-source large language models has become popular, and Alibaba has pledged at least US$53 billion over the next three years to invest in AI infrastructure. Alibaba owns the Post. Hong Kong would be able to play a role in AI development thanks to its capital resources, research capabilities, access to mainland China, government support and legal framework, Huang said. 'Hong Kong has top-tier researchers and universities, alongside the Hong Kong government also providing a lot of support for innovation, alongside the city's strong legal system,' he said. 'It is able to provide not only the capital resources, but also the human resources and the carbon for AI development.'
Finextra
14-06-2025
- Business
- Finextra
Why the Smartest Fintechs Are Scaling with AI Agents – Not Headcount: By David Weinstein
For the better part of a decade, fintech growth has followed a familiar trajectory: secure funding, hire aggressively, and scale fast in pursuit of market traction. It worked. High-performing teams, ambitious roadmaps, and well-capitalised burn rates became the standard operating model for any startup with global aspirations. But that playbook is starting to look outdated. Today's most forward-thinking fintechs are flipping the script. Instead of scaling with people or piecemeal software, today's most advanced fintechs are scaling with context-aware AI infrastructure, enabling autonomous agents to operate with memory, relevance, and the ability to adapt across time. In other words, the smartest fintechs aren't just hiring more people, they're designing for a world of leverage. From Chatbots to Autonomous Operators To be clear, this isn't about adding another chatbot to the support queue or slapping GPT on top of a FAQ. The new generation of AI agents are far more capable. These aren't just reactive tools dropped into workflows - they're embedded, active participants in how work gets done. They're not replacing human judgment, but taking over the repetitive execution that bogs it down. By operating within a structured, evolving knowledge graph, these agents access the right context, perform tasks across systems, and maintain continuity over time so that human operators can stay focused on what matters: discernment, creativity, and strategic direction. Imagine an agent that scans customer interactions across CRM, support, and marketing tools, then identifies churn risks and recommends retention strategies - autonomously. Or a compliance agent that tracks regulatory changes, audits internal data for alignment, and generates draft reports ready for human review. Or a trading operations agent that adjusts portfolio models based on real-time market signals, without needing constant human input. These agents aren't sitting in isolation. They're embedded into workflows, triggering cross-functional processes and reducing the friction that typically builds up between tools, teams, and data. And because they can run 24/7 without fatigue or context switching, they give small teams the operational capacity of much larger ones - without the organisational drag. Asymmetrical Leverage in Action The real unlock here is asymmetry. Traditional scaling is linear: more people, more output. Agent-first scaling is exponential: more intelligence per task, more value per person. For founders and operators, this is a fundamental shift in how work gets done. Take a UK-based neobank that recently rolled out an internal agent stack to manage financial operations. Instead of adding headcount to reconcile transactions, generate audit trails, and update internal dashboards, they deployed agents to handle these tasks end-to-end. As a result, a finance team of three now operates like a team of ten - not because they're working longer hours, but because the agents are doing the coordination, tracking, and formatting in the background. Or consider a US-based lending platform where customer service agents used to toggle between five tools to resolve one query. Now, an agent sits between those tools, compiles a customer's profile in seconds, drafts the reply, and even pre-fills CRM updates. One team member can now do what previously took three - and they can focus on building relationships, not piecing together data. This isn't just about cutting costs or doing more with less. It's about restoring human attention to where it matters most: judgment, creativity, strategic insight. By eliminating the constant cognitive drain of fragmented systems and shallow coordination work, agent-based infrastructure gives teams space to think, explore, and act with clarity. Why Now? The Tech Has Caught Up If this sounds too good to be true, it would've been - even 18 months ago. But recent advances in large language models, retrieval-augmented generation (RAG), and agent frameworks have changed the game. It's now possible to build AI agents that navigate APIs, evolve through feedback, and reason across a live context map - not as brittle automations, but as strategic actors. Crucially, these aren't brittle rule-based bots that break when the environment changes. The new wave of agents are adaptable. They don't just follow instructions - they understand objectives. That makes them suitable for high-change, high-ambiguity environments like fintech, where requirements shift, tools evolve, and edge cases are the norm. And because many startups are already operating in cloud-native environments with modern APIs and loosely coupled services, they're perfectly positioned to adopt agent-based infrastructure. In fact, it's often easier for an early-stage fintech to build an agent-powered back office than it is for a traditional player to untangle their legacy systems. Rethinking Operational Architecture For founders, COOs, and Chiefs of Staff, the implication is clear: if you're still building operational capacity by adding headcount, you're likely leaving leverage on the table. The question is no longer how many people do we need? - it's what do we want to automate, augment, or offload entirely? That starts with a mindset shift. Designing operations around agents means rethinking your company as an AI-native system. That means codifying your data into structured semantic graphs, enabling cross-agent collaboration, and building feedback loops where agents not only automate but adapt, reflect, and grow - just like a human team would, but faster. It also means building in feedback loops. The best agent-first teams treat their AI systems like new hires: onboard them, train them, review their output, and let them improve over time. This isn't 'set and forget' automation. It's collaborative infrastructure that evolves alongside the business. The reward? An operational stack that scales without ballooning costs or headcount. A company that can punch above its weight in terms of execution. And a team that spends more time solving problems and less time chasing updates or managing handoffs. The Next Fintech Success Stories We're already seeing the early signs of this shift. The most operationally intelligent fintechs - often the ones that look surprisingly lean from the outside - are quietly using agents to do the work of entire departments. They don't brag about it in pitch decks. They don't need to. Their advantage shows up in faster execution, cleaner operations, and happier teams. This doesn't mean people are obsolete. Far from it. But the role of humans in fintech is changing. It's no longer about scaling output through hiring. It's about designing systems that multiply the value of every person you do hire. That's the essence of leverage. And in a sector where margins are tight, competition is fierce, and compliance is non-negotiable, it could be the difference between treading water and building a category-defining business. Conclusion: Build the System, Not Just the Team In fintech, growth has historically been a headcount game. But that era is ending. The companies that succeed over the next five years won't be the ones with the biggest teams - they'll be the ones with the smartest infrastructure. Autonomous agents offer a new path: one where adaptability scales faster than bureaucracy, and intelligence compounds faster than payroll. So if you're building a fintech startup in 2025, ask yourself: are you hiring for leverage - or designing for it? Because the smartest teams aren't growing by the dozen. They're growing by the agent.
Tahawul Tech
13-06-2025
- Tahawul Tech
Will you love your AI twin?
Imagine discovering that somewhere in the expanded universe, a version of you solves problems faster, thinks clearer, and never needs coffee. In Spider-Man: Into the Spider-Verse, alternate versions of one character collide in unexpected, often uncomfortable ways. As AI becomes more sophisticated, we may soon be working alongside digital twins of ourselves—replicas that mirror our skills and, sometimes, outshine them. In our workplaces, AI is rapidly evolving from a supportive assistant to an autonomous agent—and just over the horizon is the emerging concept of a digital AI twin. With today's agentic AI, systems can independently execute an entire workstream, such as project management, with little need for a human in the loop once the training phase is complete. Tomorrow, it is highly probable that an AI digital twin—an AI-powered replica of your human expertise—will work alongside you. What challenges and opportunities could we face as we are twinned with AI? What relationship will you have with your new work twin—love, hate, envy? What risks and opportunities lie ahead in this never-say-never moment? Drawing on human twin psychology, having a twin can be both a blessing and a burden. Just like a human twin can offer emotional support, your AI twin could act as a reliable workmate, reducing stress and enhancing capabilities such as super-fast logical thinking. On the darker side, it could start to feel like it's taking over—performing better, getting more recognition, even overshadowing you. But is this really possible? Let's dig into the tech side. Where are we now? Autonomous AI agents are already in use across the business world and in our personal lives. Think self-driving cars, advanced fraud detection, drug discovery, and customer service chatbots that actually help. Here's how an agentic AI Help Desk Assistant might work: it could personalize support based on what your company knows about your setup. Say your Teams app crashes every time you try to join a meeting. You submit a help desk ticket before a crucial early morning call. An AI agent receives it, reviews your computer's hardware and software update status, researches recent Microsoft bug fixes, identifies the issue from the IT-approved list, and installs the update. Voila! You no longer have to wait for an IT agent to become available—and the AI could fix the problem faster than a human could. Where could we go in the future? In the next 5–10 years, we're likely to see more sophisticated and general-purpose agentic AI systems that can: · Manage more long-term goals, such as planning and executing projects over weeks or months · Integrate deeply with healthcare providers · Oversee smart home systems with advanced control · Support the broader adoption of self-driving cars. Back to Twinning AI agents will also become more capable collaborators, especially in complex fields such as research, healthcare, and engineering. For instance, scientists could soon rely on agentic AI to autonomously explore datasets, generate hypotheses, and suggest experiments. Google is already experimenting with this via AI co-scientist, a virtual collaborator built with Gemini 2.0. So the question remains—will you love your AI twin? As human twins grow up, they learn values, guardrails, and decision-making frameworks from their families. Who will teach your AI twin what matters to you? Will you consent to share the personal information needed to learn from you? Will our privacy standards expand again? And when you leave your job, will your AI twin come with you, or stay behind? As agentic AI becomes more autonomous, it raises serious questions about accountability, transparency, and ethics. We'll need robust ethical frameworks, explainable AI models, and governance systems to match the technology's growing capability. Agentic AI represents a leap forward—unlocking systems that can operate with greater autonomy, adaptability, and judgment. While today's systems are still relatively narrow in scope, the next decade is likely to bring broader, more general-purpose agents that redefine how we think about and interact with machines and how machines can behave on their own. The time to grapple with these questions is now, so we can balance innovation with ethics and ensure that agentic AI aligns with our values and delivers real benefits to humanity. Authored by Tim Walmsley, Head of Transformation and Change Management, APCO MENA, and Kristy Lapidus, Senior Director AI & Digital Transformation, APCO Gagen MacDonald.
