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AI Sandbox Risks & Wins: 30 Tools & 7 Real-Life Examples
Interest in AI sandboxes has surged in recent months. They provide secure environments to develop, test, and deploy AI models without risking sensitive data or system stability.
Top 10 Agentic AI ERP Systems & 6 Solutions
Gartner predicts that by 2028, one-third of enterprise software will include agentic AI, making up to 15% of daily decisions autonomous. Agentic AI ERP refers to AI agents integrated into Enterprise Resource Planning systems, enabling data analysis, prediction, and autonomous actions.
Large World Models: Use Cases & Real-Life Examples
Artificial intelligence has advanced significantly with the development of large language models; however, these systems continue to struggle to comprehend and interact with the physical world. Text alone cannot capture spatial relationships, dynamic environments, or the causal impact of actions, thereby limiting progress in fields such as robotics, healthcare, and autonomous systems.
MCP Security: Best Practices and Avoid Common Pitfalls
The model context protocol (MCP), pioneered by Anthropic, is quickly becoming the go-to standard for connecting large language models (LLMs) to the outside world. But the same simplicity that makes MCP so powerful also makes it risky.
Agentic Mesh: The Future of Scalable AI Collaboration
While much has been written about agent architectures, real-world production-grade implementations remain limited. Building on my earlier post about A2A fundamentals, this piece highlights the agentic AI mesh, a concept introduced in a recent McKinsey.
The 7 Layers of Agentic AI Stack
The rise of agentic AI has introduced a technology stack that extends well beyond simple calls to foundation-model APIs. Unlike traditional software stacks, where value often concentrates at the application tier, the agentic AI stack distributes value more unevenly. Some layers offer strong opportunities for differentiation and moat building, while others are rapidly becoming commoditized.
AI Agents for Competitive Intelligence: Tools and Applications
The competitive intelligence landscape is shifting rapidly. MarketsandMarkets projects the global AI agent market will grow from USD 5.1 billion in 2024 to USD 47.1 billion by 2030, at a CAGR of 44.8%. Traditional methods, quarterly reports, and manual research are being replaced by AI agents that continuously track competitors, delivering insights in real-time.
Large Quantitative Models: Applications & Challenges
Modern systems are becoming too complex for traditional statistical analysis, as institutions now handle massive datasets, including patient data, weather data, and financial market data. Large quantitative models (LQMs) help by processing these datasets, integrating structured and unstructured data, and applying predictive modeling to uncover patterns and provide data-driven insights that traditional methods cannot deliver.
Best AI Agents for Workflow Automation
We researched the leading AI agent platforms for workflow automation, analyzing their documentation, feature sets, integration capabilities, and publicly available customer implementations. There are 4 ways to implement AI agents for workflow automation. Top 10 AI Agents for Workflow Automation *Starting price per month ** Reviews are based on Capterra and G2.
Audience Simulation: Can LLMs Predict Human Behavior?
In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence.