According to DeepLearning.AI, Christoph Meyer, Principal AI Scientist, and Lars Heling, Senior Knowledge Engineer at SAP, presented at AI Dev 25 x NYC on leveraging knowledge graphs to boost AI agent discovery and execution. Their session demonstrated that while large language models (LLMs) provide fluency for AI agents, knowledge graphs deliver essential semantic and process context, enabling agents to accurately discover and securely invoke the appropriate tools and APIs within complex enterprise environments. They illustrated practical techniques such as semantic retrieval and process-aware API connectivity, showing how these align with the Model Context Protocol (MCP) standard for enterprise AI. A live demo showcased an agent using these methods for process automation, highlighting direct business opportunities in improving workflow automation, reducing operational risk, and accelerating integration with enterprise software ecosystems (Source: DeepLearning.AI, Twitter, Dec 18, 2025).

According to Anthropic (@AnthropicAI), the company is donating its Model Context Protocol (MCP) to the Agentic AI Foundation (AAIF), which operates under the Linux Foundation. Over the past year, MCP has become a foundational protocol for agentic AI applications, enabling interoperability and secure context-sharing among AI agents. This move ensures MCP remains open-source and community-driven, fostering broader adoption and collaborative innovation within the AI industry. Industry analysts note this step will accelerate the development of standardized frameworks for agentic AI, opening new business opportunities for companies building agent ecosystems and multi-agent systems (Source: Anthropic, 2025).

According to AnthropicAI, the latest post on the Anthropic Engineering blog provides actionable strategies for developing more efficient AI agents capable of managing a greater number of tools while minimizing token usage. The article highlights the use of the Model Context Protocol (MCP), which enables code execution within agents, optimizing resource consumption and improving scalability for enterprise AI applications. This development creates significant business opportunities for companies seeking to deploy AI agents that process complex tasks efficiently and cost-effectively, as verified by Anthropic Engineering Blog (source: https://www.anthropic.com/engineering/code-execution-with-mcp).

According to Andrew Ng (@AndrewYNg), a new short course titled 'Build AI Apps with MCP Servers: Working with Box Files' has launched, developed in partnership with Box and taught by their CTO, BenAtBox. The course demonstrates how the Model Context Protocol (MCP) can eliminate the need for custom integration code by allowing AI applications to offload file operations to dedicated servers. Learners will gain practical skills in building LLM-powered document processing apps using the Box MCP server, leveraging tools that large language models (LLMs) can access directly. The curriculum also covers designing multi-agent systems with Google's Agent Development Kit (ADK) and orchestrating workflows via the Agent2Agent (A2A) protocol. This approach streamlines file access for AI applications, accelerating secure enterprise document processing and opening up new business opportunities in AI-driven workflow automation, as cited by Andrew Ng's official Twitter announcement.

According to @perplexity_ai, workspace admins can now leverage the Model Context Protocol (MCP) beta to build custom deep research connectors. This empowers organizations to integrate proprietary systems and various third-party apps, enabling teams to search, reason, and act on internal knowledge alongside web results and pre-built connectors (source: @perplexity_ai, 2024-06). Available to Team, Enterprise, Edu admins, and Pro users, this advancement opens new business opportunities for organizations seeking to enhance AI-driven knowledge management and workflow automation.