News from the AI & ML world
DeeperML - #multiagentsystems
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Derek Egan@AI & Machine Learning
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Google Cloud is enhancing its MCP Toolbox for Databases to provide simpler and more secure access to enterprise data for AI agents. Announced at Google Cloud Next 2025, this update includes support for Model Context Protocol (MCP), an emerging open standard developed by Anthropic, which aims to standardize how AI systems connect to various data sources. The MCP Toolbox for Databases, formerly known as the Gen AI Toolbox for Databases, acts as an open-source MCP server, allowing developers to connect GenAI agents to enterprise databases like AlloyDB for PostgreSQL, Spanner, and Cloud SQL securely and efficiently.
The enhanced MCP Toolbox for Databases reduces boilerplate code, improves security through OAuth2 and OIDC, and offers end-to-end observability via OpenTelemetry integration. These features simplify the development process, allowing developers to build agents with the Agent Development Kit (ADK). The ADK, an open-source framework, supports the full lifecycle of intelligent agent development, from prototyping and evaluation to production deployment. ADK provides deterministic guardrails, bidirectional audio and video streaming capabilities, and a direct path to production deployment via Vertex AI Agent Engine. This update represents a significant step forward in creating secure and standardized methods for AI agents to communicate with one another and access enterprise data. Because the Toolbox is fully open-source, it includes contributions from third-party databases such as Neo4j and Dgraph. By supporting MCP, the Toolbox enables developers to leverage a single, standardized protocol to query a wide range of databases, enhancing interoperability and streamlining the development of agentic applications. New customers can also leverage Google Cloud's offer of $300 in free credit to begin building and testing their AI solutions.
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@www.developer-tech.com
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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.
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Nitika Sharma@Analytics Vidhya
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China's Manus AI, developed by Monica, is generating buzz as an invite-only multi-agent AI product. This AI agent is designed to autonomously tackle complex, real-world tasks by operating as a multi-agent system. It utilizes a planner optimized for strategic reasoning, and an executor driven by Claude 3.5 Sonnet, incorporating code execution, web browsing, and multi-file code management.
The AI agent has sparked considerable global attention, igniting discussions about its technological and ethical implications, as well as its potential impact on the AI landscape. Manus reportedly outperformed OpenAI's o3-powered Deep Research agent on benchmarks, as showcased on the Manus website, leading some to believe it is among the most effective autonomous agents currently available. However, there is some skepticism due to it appearing to be a Claude wrapper with a jailbreak and tools optimized for the GAIA benchmark.
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