News from the AI & ML world
DeeperML - #enterpriseai
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Matt Marshall@AI News | VentureBeat
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OpenAI has unveiled a new suite of APIs and tools aimed at simplifying the development of AI agents for enterprises. The firm is releasing building blocks designed to assist developers and businesses in creating practical and dependable agents, defined as systems capable of independently accomplishing tasks. These tools are designed to address challenges faced by software developers in building production-ready applications, with the goal of automating and streamlining operations.
The newly launched platform includes the Responses API, which is a superset of the chat completion API, along with built-in tools, the OpenAI Agents SDK, and enhanced Observability features. Nikunj Handa and Romain Huet from OpenAI previewed new Agents APIs such as Responses, Web Search, and Computer Use, and also introduced a new Agents SDK. The Responses API is positioned as a more flexible foundation for developers working with OpenAI models, offering functionalities like Web Search, Computer Use, and File Search. Recommended read:
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Matt Marshall@AI News | VentureBeat
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OpenAI has unveiled its Agents SDK, along with a revamped Responses API, built-in tools, and an open-source SDK. These tools simplify the development of AI agents for enterprise use by consolidating the complex ecosystem into a unified framework. This platform allows developers to create AI agents capable of performing tasks autonomously. The Responses API integrates with OpenAI’s existing Chat Completions API and Assistants API to assist in agent construction, while the Agents SDK helps users orchestrate both single and multi-agent workflows.
This initiative addresses AI agent reliability issues, recognizing that external developers can offer innovative solutions. The SDK reduces the complexity of AI agent development, enabling projects that previously required multiple frameworks and specialized databases to be achieved through a single, standardized platform. This marks a critical turning point as OpenAI recognizes the value of external contributions to the advancement of AI agent technology. With web search, file search, and computer use integrated, the Responses API enables agents to interact with real-world data and internal proprietary business contexts more effectively. Recommended read:
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Ryan Daws@AI News
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ServiceNow has announced the release of its Yokohama platform, marking a significant advancement in the integration of AI agents within enterprise workflows. The platform introduces preconfigured AI agents designed to enhance productivity across various sectors, offering seamless integration and immediate benefits. New features facilitate the building, onboarding, and management of AI agents, aiming to broaden the adoption of AI-driven solutions throughout organizations. This release is part of ServiceNow's strategy to double down on AI investments, particularly in agentic AI capabilities, which are designed to automate tasks and improve workflows across CRM, HR, IT, and other departments.
The Yokohama platform features ServiceNow Studio, a centralized environment for no-code, low-code, and pro-code developers to create and manage agentic applications. This tool aims to streamline enterprise automation and reduce adoption barriers. New AI agents have been added, including a SecOps agent for security operations, autonomous change management agents, and a network test and repair agent. These agents aim to automate repetitive tasks, improve network performance, and free up human employees to focus on more strategic work. ServiceNow also acquired Moveworks to expand its AI capabilities into enterprise search, improving information access for employees. Recommended read:
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@Latest from Tom's Guide
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AI News | VentureBeat
, venturebeat.com
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Anthropic's Claude AI platform is generating significant discussion, particularly around its coding capabilities and the future role of AI in scientific advancement. Anthropic has released Claude 3.7 Sonnet, setting new benchmarks for coding performance and positioning itself as the leading LLM for enterprise applications. This development comes alongside the launch of Claude Code, an AI coding agent designed to accelerate application development. Furthermore, Anthropic recently secured $3.5 billion in funding, raising its valuation to $61.5 billion, solidifying its position as a key competitor to OpenAI.
Mike Krieger, chief product officer at Anthropic, predicts that within three years, software developers will primarily be reviewing AI-generated code. This shift raises questions about how entry-level developers will gain the necessary experience in a field where reviewing code demands expertise. However, the optimistic views of Anthropic CEO Dario Amodei are facing scrutiny as Hugging Face co-founder Thomas Wolf challenges the notion of a "compressed 21st century," arguing that current AI systems may produce conformity rather than the revolutionary scientific breakthroughs Amodei envisions. Recommended read:
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@Salesforce
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Salesforce
, BigDATAwire
Salesforce President and Chief Engineering & Customer Success Officer, Srini Tallapragada, recently addressed the challenges CIOs face when adopting Agentic AI. The increasing interest in AI strategies highlights the critical need for strategic planning and addressing the complexities of integrating Agentic AI into existing enterprise workflows. Salesforce is actively refining its agentic AI platform with a flexible database to meet this growing demand.
Salesforce's agentic AI platform, bolstered by updates to Agentforce, aims to enable more proactive and autonomous AI agents. A key element is providing agents with the necessary context to operate independently. Salesforce emphasizes "topics, instructions, and guardrails" as part of the learning process for these agents, mirroring the onboarding process for human employees. Muralidhar Krishnaprasad, president and CTO of Salesforce, highlighted the importance of teaching agents what to do, emphasizing that data and actions alone are insufficient for success. Recommended read:
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Lindsey Wilkinson@CIO Dive - Latest News
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AI News | VentureBeat
, CIO Dive - Latest News
Enterprises are rapidly adopting AI agents, driven by the expectation of high returns on investment. A recent PagerDuty report, surveying 1,000 IT and business executives, revealed that over 60% anticipate a return of over 100% on their agentic AI investments, with an average expected return of around 171%. Optimism is even higher among U.S.-based companies, where decision-makers project returns closer to 192%. This enthusiasm is fueling a faster adoption rate for AI agents compared to generative AI, with over 90% of those surveyed believing agents will be implemented more quickly.
While excitement surrounds agentic AI, enterprises are also mindful of lessons learned from initial generative AI deployments. Challenges with realizing ROI due to rushing implementations, overspending, and lacking proper infrastructure have prompted a more cautious and strategic approach to agentic AI. According to a Gartner report, global generative AI spending is projected to reach $644 billion in 2025, with hardware accounting for a significant portion of this investment. Despite the potential benefits, decision-makers express concerns about data security, privacy, and integration with existing systems, highlighting the importance of establishing robust security measures and governance frameworks for agentic AI deployments. Recommended read:
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@www.marktechpost.com
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Windows Copilot News
, www.marktechpost.com
A new wave of AI-powered browser-use agents is emerging, with companies like OpenAI, Convergence, Google, Anthropic, and Microsoft developing solutions. These agents aim to transform how enterprises interact with the web by autonomously navigating websites, retrieving information, and completing tasks. For example, OpenAI's Operator focuses on consumer-friendly web automation, while Convergence's Proxy offers free limited use and a paid unlimited access option.
However, early testing reveals significant gaps between promise and performance. While consumer-focused applications like ordering pizza or booking game tickets have garnered attention, the primary developer and enterprise use cases are still being determined. Experts suggest that these agents may find their niche in time-consuming web-based tasks like price comparisons and hotel booking or be used in combination with other tools like Deep Research, where companies can then do even more sophisticated research plus execution of tasks around the web. AI agents are autonomous software entities that perceive their surroundings, process data, and take action to achieve specified goals. They differ from traditional software by employing machine learning and natural language processing for decision-making, allowing them to evolve over time. Key characteristics include autonomy, adaptability, interactivity, and context awareness. The evolution of AI agents represents a shift from rule-based systems to systems that learn and adapt. Recommended read:
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