How MCP is transforming AI models into connected systems

New research from Salesforce AI Research: MCP-Universe benchmark provides a deeper understanding of how AI agents perform on tasks. The findings are being used to improve their frameworks and the implementation of their MCP tools. Learn more here

You may have heard the term “RAG” in AI conversations, but what does it actually mean? RAG, or Retrieval-Augmented Generation, lets AI look things up before answering, rather than relying only on what it memorised during training. This makes their outputs traceable, verifiable, and grounded in real data/facts. This is a crucial step in creating trust in what AI tells you, whether it’s for customer support, internal reports, or business insights. It is essentially a way to make AI more accountable and verifiable. Learn more and see practical examples here: https://lnkd.in/eMBGqk2J

New inspiring AI insights from our colleague, Branislav Popović, AI & ML Expert and Principal Research Fellow! Learn how the model context protocol enhances AI’s strategic agility through context-aware orchestration, and why choosing the right client, balancing performance trade-offs, and ensuring strong governance are essential for effectively deploying adaptive, intelligent AI systems. Find out more here: https://lnkd.in/dtwmJNjw

Prior Labs TabPFN-2.5: Pioneering Scale and Speed in Tabular AI Foundation Models ired of waiting hours for your machine learning models to train? The game is changing for tabular data. ⏱️➡️⚡ Meet TabPFN-2.5, a revolutionary AI model that requires ZERO training. It delivers incredibly accurate predictions in under a second, transforming everything from marketing forecasts to financial fraud detection. We broke down how this groundbreaking technology works, how it stacks up against giants like XGBoost, and its real-world impact on the U.S. workforce. This is the future of data science, and it's happening now. 🔗 Read the full deep-dive here: https://lnkd.in/dPQ5HAKE #AI #MachineLearning #TabPFN #DataScience #PriorLabs #Tech #Innovation #FinTech #MarTech #FutureOfAI #NoTrainingAI

While studying a multi-agent AI paper, I came across the fact that the researchers used Microsoft AutoGen to implement collaborative agent workflows. This led me to study AutoGen in depth, and I wrote an article: “AutoGen — The Multi-Agent AI Framework That Thinks Like a Team." Key takeaways from the article: 1. GroupChat: Enables multiple agents to communicate, debate, and collaborate in a shared conversation. 2. Specialized Roles: Agents like Planner, Critic, and Summarizer work together to tackle complex tasks. 3. LLM Integration: Leverages OpenAI models and can also integrate other language models. 4. Human-in-the-loop & Tools: Agents can receive feedback, use multiple tools, and share context for smarter outcomes. Read the full article here: https://lnkd.in/gzZ3_V6e

Too often, organizations focus on building AI strategies without first strengthening the foundation that truly makes AI work — their data. Ask yourself: are you relying on flat tables, or have you invested in fully relational, traceable datasets that give your models real depth and context? And just as importantly, does your AI tooling integrate human logic and understanding, or is it operating in isolation? Andy Brennan explores these critical questions in IBISWorld’s latest article on building smarter AI through smarter data. 👉 Read more here: https://lnkd.in/e5H3s-Hz

Could LLMs Be The Route To Superintelligence? — With Mustafa Suleyman. In an interview with Alex Kantrowitz the CEO of Microsoft AI and the head of the company’s new superintelligence team, discusses their push toward “humanist superintelligence” and what changes after its latest OpenAI deal. Suleyman has expressed nuanced optimism about large language models (LLMs) as a foundational technology in AI development. However, he does not view current LLMs alone as a straightforward or linear route to superintelligence—defined as AI exceeding human-level performance across all tasks. https://lnkd.in/ef6yh25j

In today’s fast-moving landscape of generative AI, simply relying on large language models (LLMs) trained on static datasets often isn’t enough. That’s where Retrieval-Augmented Generation (RAG) comes in — a technique that combines retrieval of external, relevant information with generation by an LLM, helping bring more accuracy, relevance and up-to-date context to the output. Here’s why RAG matters: • It enables the model to pull in domain-specific or proprietary data (e.g., internal knowledge bases, up-to-date documents) after training, rather than having to retrain the model every time the knowledge changes. • It helps reduce “hallucinations” — i.e., plausible‐but‐wrong answers from an LLM — by grounding generation in retrieved evidence. • It opens up new enterprise possibilities: e.g., customer service bots, document summarisation, domain-specialised assistants, all leveraging your organisation’s own data. Key components of a RAG system include: 1. A retrieval mechanism (for example, vector-searching a document corpus) 2. A generation step (the LLM) that uses both the user’s query + retrieved context 3. Continuous augmentation of the knowledge base (so that the information remains fresh). Challenges & things to watch out for: • Retrieval quality matters: if you bring in irrelevant or misleading documents, you risk worse outcomes. • Enterprise data governance, security & compliance become critical when you open the retrieval to internal or proprietary content. • Design trade-offs: how many retrieved documents to feed? How to rank them? How to prompt the LLM for best use of context? BentoML Bottom line: If you work in AI, data, knowledge management or customer-facing automation, RAG is a design pattern worth understanding and adopting. It’s not just “another model” — it’s about bridging external (and evolving) knowledge with generative technology. I’d love to hear how others are using or thinking about RAG in their teams: Are you building knowledge bots, document assistants, domain-specific generative systems? What has worked / not worked? #GenerativeAI #RAG #AI #KnowledgeManagement #LLM #Innovation https://lnkd.in/df2-jhH4 https://lnkd.in/dsefHUHu https://lnkd.in/dx9_HhUP

Are your AI tools operating in silos? Businesses are realizing that large language models aren’t enough on their own, especially when data lives across disconnected systems. Learn how the Model Context Protocol (#MCP) could be the missing piece of the puzzle that lets #AI agents work alongside people, securely access tools, and drive automated, multistep workflows. https://ow.ly/C0Qz50XnwiI