News from the AI & ML world
DeeperML - #workflowautomation
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Lyzr Team@Lyzr AI
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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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Alexey Shabanov@TestingCatalog
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Analytics Vidhya
, TestingCatalog
AI agents are rapidly transforming how work gets done by automating and streamlining a variety of workflows. These intelligent systems are designed to handle tasks ranging from managing schedules, emails, and notes, as exemplified by Genspark's new AI Secretary feature, to providing personalized customer engagement in the automotive retail sector, demonstrated by Impel's use of fine-tuned LLMs. The core advantage of agentic AI lies in its capacity for autonomous decision-making and enhanced customer experiences powered by AI-driven solutions. Impel, for instance, optimizes automotive retail customer connections through personalized experiences at every touchpoint, utilizing Sales AI to provide instant responses and maintain engagement during the car-buying journey.
The development of agentic AI extends to the realm of IoT, where these agents are poised to enable autonomous, goal-driven decision-making. This is particularly relevant in smart homes, cities, and industrial systems, where AI agents can proactively address network issues, strengthen security, and improve overall productivity. Agentic AI marks a structural shift from traditional AI, transitioning from task-specific and supervised models to autonomous agents capable of real-time decisions and adaptation. These agents possess memory, autonomy, task awareness, learning, and reasoning abilities, allowing them to operate with minimal human intervention. However, the effectiveness of AI agents hinges on accurate monitoring strategies and their ability to navigate complex tasks. To ensure reliability in real-world scenarios, benchmarks like WebChoreArena are being developed to challenge agents with memory-intensive and reasoning-intensive scenarios. Building robust conversational AI agents also requires overcoming limitations in existing frameworks. The Rasa platform offers an alternative approach through process calling, enabling the creation of reliable, process-aware, and easily debuggable conversational agents. This method addresses issues such as loss of conversational context and poor adherence to business processes, ensuring that AI agents can consistently guide users through predetermined workflows. Recommended read:
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@aithority.com
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AiThority
, AI News | VentureBeat
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Agentic AI is rapidly transforming workflow orchestration across various industries. The rise of autonomous AI agents capable of strategic decision-making, interacting with external applications, and executing complex tasks with minimal human intervention is reshaping how enterprises operate. These intelligent agents are being deployed to handle labor-intensive tasks, qualitative and quantitative analysis, and to provide real-time insights, effectively acting as competent virtual assistants that can sift through data, work across platforms, and learn from processes. This shift represents a move away from fragmented automation tools towards dynamically coordinated systems that adapt to real-time signals and pursue outcomes with minimal human oversight.
Despite the potential benefits, integrating agentic AI into existing workflows requires careful consideration and planning. Companies need to build AI fluency within their workforce through training and education, highlighting the strengths and weaknesses of AI agents and focusing on successful human-AI collaborations. It is also crucial to redesign workflows to leverage the capabilities of AI agents effectively, ensuring that they are integrated into the right processes and roles. Furthermore, organizations must not neglect supervision, establishing a central governance framework, maintaining ethical and security standards, fostering proactive risk response, and aligning decisions with wider company strategic goals. American business executives are showing significant enthusiasm for AI agents, with many planning substantial increases in AI-related budgets. A recent PwC survey indicates that 88% of companies plan to increase AI-related budgets in the next 12 months due to agentic AI. The survey also reveals that a majority of senior executives are adopting AI agents into their companies, reporting benefits such as increased productivity, cost savings, faster decision-making, and improved customer experiences. However, less than half of the surveyed companies are rethinking operating models, suggesting that there is still untapped potential for leveraging AI agents to fundamentally reshape how work gets done. Recommended read:
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Rowan Cheung@The Rundown AI
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AI is rapidly transforming how businesses operate, particularly in streamlining data processing and automation. Mid-sized enterprises are leveraging AI data processing capabilities to automate repetitive tasks, extract valuable insights from extensive datasets, and minimize errors associated with manual processes. UiPath has launched Agentic Automation, a platform that expands the role of digital workers beyond routine tasks to intelligent AI agents capable of reasoning, adapting, and collaborating more like humans. This shift enables intelligent collaboration between AI, robots, and people, accelerating decision-making and boosting productivity gains across various enterprise environments.
UiPath's platform, featuring Maestro, coordinates AI agents, robots, and humans across business processes, transforming static workflows into dynamic streams of events that adapt to changing conditions in real-time. According to UiPath CEO Daniel Dines, agentic automation combines Robotic Process Automation (RPA), AI models, and human expertise into cohesive workflows. This integration allows for understanding, improving, and automating diverse workflows, thereby driving significant enterprise efficiency. The goal is to empower people to focus on meaningful work by freeing them from mundane tasks. FutureHouse has also entered the scene with a new platform featuring four "superintelligent" AI agents—Crow, Falcon, Owl, and Phoenix—designed to assist scientists in navigating the vast amount of research literature. These agents are reportedly more accurate and precise than major frontier search models and even PhD-level researchers in literature search tasks. The AI agents can identify unexplored mechanisms, find contradictions in literature, analyze experimental methods, customize research pipelines, and reason about chemical compounds. This innovation promises to accelerate scientific discovery by automating and enhancing the research process. Recommended read:
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@news.microsoft.com
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Ken Yeung
, The Dataiku Blog
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AI agents are rapidly transforming business operations across various industries, with companies like HubSpot, Writer, and Microsoft leading the charge in developing and deploying these intelligent systems. These agents are designed to automate tasks, improve efficiency, and empower businesses, particularly small and medium-sized businesses (SMBs), to compete with larger enterprises. AI agents represent a significant shift in how work is approached, offering the potential to streamline processes and unlock new levels of productivity.
Microsoft is actively working to bring AI agents to organizations, emphasizing the importance of adopting an "AI-first mindset" to remain competitive. HubSpot has introduced new AI-powered tools built on its Breeze AI platform, including customer assistants, knowledge base tools, prospecting assistants, and content generators, all designed to help SMBs scale their go-to-market efforts. Writer has launched AI HQ, a centralized hub for enterprises to orchestrate agent-powered work, featuring a low-code Agent Builder and a library of ready-to-use agents for tasks across various sectors like finance and healthcare. Beyond business applications, AI agents are also making strides in other fields. DeepMind has developed an AI that teaches itself to play Minecraft from scratch, demonstrating the ability to learn complex maneuvers without human guidance. Google DeepMind has created AI agents like AMIE and Co-Scientist that are outperforming human doctors in diagnostic accuracy and making independent scientific discoveries. Furthermore, efforts are underway to secure agentic AI systems from threats, ensure model context, and build robust AI architectures to support successful agent implementation. Recommended read:
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