Multi-agent AI agent personality shapes outcomes in collaborative and negotiation workflows but not in structured coding, ...

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 ...

Traditional processes used to discover new materials are complex, time-consuming, and costly, often requiring years of sustained effort. Recent advances in large language models (LLMs) have ...

Paulo Arruda discusses Shopify’s evolution in AI adoption, moving from simple chat tools to a sophisticated swarm of specialized agents. He explains the transition from massive "all-in-one" prompts to ...

A new framework called SkillWeaver tackles AI agent tool routing by skipping full-library loading, cutting token use 99% on ...

Researchers at Google and MIT have conducted a comprehensive analysis of agentic systems and the dynamics between the number of agents, coordination structure, model capability, and task properties.

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 ...

Michael Wegmüller has more than 20 years of experience in AI. He is Co-Founder of Artifact SA and a widely recognized AI business expert. Large language models (LLMs) like OpenAI’s ChatGPT have ...

Traditional RAG systems struggle bridging structured SQL databases and unstructured document collections (a challenge we call the modality gap), leading to incomplete reasoning and hallucinations.

Retrieval-augmented generation enhances the performance of AI agents by expanding their recall. It can do this in three ...