The landscape of artificial intelligence is undergoing a significant transformation. As the capabilities of large language models grow, we are beginning to see a shift away from isolated ...

What if you could design a system where multiple specialized agents work together seamlessly, each tackling a specific task with precision and efficiency? This isn’t just a futuristic vision—it’s the ...

How event-driven design can overcome the challenges of coordinating multiple AI agents to create scalable and efficient reasoning systems. While large language models are useful for chatbots, Q&A ...

We just can’t seem to help ourselves. Our current infatuation with multi-agent systems risks mistaking a useful pattern for an inevitable future, just as we once did with microservices. Remember those ...

Today, multi-agent systems (MAS) have emerged as transformative technologies, driving innovation and efficiency across various industries. Comprising multiple autonomous agents working collaboratively ...

The biggest challenge to AI initiatives is the data they rely on. More powerful computing and higher-capacity storage at lower cost has created a flood of information, and not all of it is clean. It ...

At the core of Industry 4.0, the smart factory integrates automation, mass customization, and self-organization into a highly ...

As artificial intelligence (AI) becomes more common in health care, from managing records to assisting with medication decisions, researchers at the Icahn School of Medicine at Mount Sinai are asking ...

Training standard AI models against a diverse pool of opponents — rather than building complex hardcoded coordination rules — is enough to produce cooperative multi-agent systems that adapt to each ...

Stanford's DeLM lets AI agents coordinate without a central controller, cutting multi-agent inference costs 50% and beating ...