Debating AI and Agentic AI with Anthropic

🚀 AI Agents vs. Workflows: Not the Same You’ve heard both terms → but they’re not interchangeable. Anthropic published an article, explaining the difference: - Workflows = predefined steps. Predictable. Like: “fetch → clean → report.” - AI Agents = self-directing. LLMs decide what to do, when, and how. Use workflows for reliability, auditability, and scale → think compliance, batch jobs. Use AI agents for flexibility, adaptation, and open-ended tasks → like research, support, or exploration. The future? Not one or the other; but orchestrating both. What are you building? Workflows? Agents? Both? https://lnkd.in/dBveJccV

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

Generative AI is the future of search, with AI-powered tools already making it easier than ever for owners to quickly access the information they need, no matter how disorganized their files are. But if all you’re doing is putting your documents into a generic AI tool, you’re probably not getting the quality responses that you need. At Gryps, we’ve developed a construction-specific knowledge graph that contextualizes all of your information, allowing our AI search tool to consistently provide relevant, reliable responses. Read our latest blog post to learn more: https://lnkd.in/ek3h3z9J

Industry data is fragmented and ALWAYS comes from different sources. Read our latest post on how Gryps creates the #owners knowledge graph to combine structured and unstructured data to power our #ai #search

👀 Want to build an AI chatbot that does more than just answer questions? Our latest step-by-step guide shows you how to build an intelligent chatbot using Anthropic's Claude #API and the new Vonage Model Context Protocol (#MCP) API. 💻 Learn how to empower your AI agent to check account balances, send messages, and interact with external services, all through conversation. It's a powerful way to create #AI assistants that can do things, not just talk about them. 👉 Ready to get started? Check out the full tutorial on our blog https://vonage.dev/4hWWMtI

I’ve always said that the entire point of having things like SDKs and CLIs are to provide ways to make the developers life easier. These kinds of things should accelerate productivity, not be things that purely drive downloads like HTTP wrappers. AI, and MCP servers, are an exciting way to help devs do work without leaving their IDE. Much like a command line application can speed up a developer, my team and I are working on a MCP server you can run with your favorite LLM. Combine it with our Documentation MCP server to get questions answered and now be able to work with your account and speed up development. We’ve got a lot of things planned through this year and the next, this is just the beginning.

I’ve been exploring the evolving world of AI lately, and it’s fascinating how every type of AI has its own mindset — different tools, different purposes, completely different ways of working. Here’s a simplified take on what I’ve learned so far. 1.Generative AI – The Creator This is the kind of AI that creates — whether it’s text, code, art, or music. Tools used: GPT, Gemini, Claude, DALL·E, Midjourney Used for: ·  ChatGPT generating text or ideas in real time. ·  GitHub Copilot writing code as developers type. 2. Multimodal AI – The All-Rounder AI that can understand multiple formats at once — text, image, video, and even audio. Tools used: GPT-4V, Gemini, CLIP, LLaVA Used for: · Google Gemini interpreting an image or chart and explaining it in context. · AI assistants summarizing scanned documents or screenshots. 3. Reasoning AI – The Thinker AI that doesn’t just predict — it reasons step by step to solve complex problems. Tools used: Claude 3.7, DeepSeek-R1, OpenAI o1, Mistral-Large Used for: · Solving logic or math problems with step-by-step thinking. · Debugging code or analyzing data through structured reasoning. 4. RAG (Retrieval-Augmented Generation) – The Researcher Combines generation with real-time retrieval — looking up external information before answering. Tools used: LangChain, LlamaIndex, Pinecone, Qdrant, Weaviate Used for: · Chatbots that pull answers from company documents. · AI tools that summarize the latest research papers or reports accurately. 5. Agentic AI – The Doer (and the Latest Game-Changer) This is the newest wave of AI — and one of the most exciting ones yet. Agentic AI doesn’t just reply — it acts. It can plan, reason, use tools, connect APIs, and even work with other AIs to complete tasks end to end. Tools used: AutoGen, CrewAI, LangGraph, OpenDevin, Meta’s Agentic Frameworks Used for: · AI systems that schedule meetings, draft emails, or analyze data automatically. · Multi-agent setups where one AI researches, another writes, and a third reviews — all on their own. The next wave of AI is already forming — with Embodied AI (robots that physically act in the world), Edge AI (smart assistants running directly on devices), and Emotional or Empathetic AI (systems that can sense and respond to human emotion). AI isn’t just one thing anymore — it’s an entire ecosystem of creators, thinkers, and doers. Exploring about it has made me even more curious about what’s coming next. #ArtificialIntelligence #MachineLearning #AgenticAI #RAG #GenerativeAI #ReasoningAI #AIInnovation #TechTrends #LearningJourney

I recently published a new article on Medium where I share some practical lessons from building AI agents. These are insights that came from real experiments, iterations, and learning along the way. If you're building AI agents or planning to, I hope this helps you avoid some common pitfalls. This is one of the most important topics to understand when building AI agents: "how to choose the right LLM for your AI agent". Medium #AI #AIAgents #LLM #AIEngineering #MachineLearning #Learnwithkavi

#Technical_Post_1 #AI What if AI could think and write like humans anywhere in the text? That’s diffusion LLMs. Meet dLLM. dLLM is an open-source library. It helps you train, run, and evaluate diffusion language models. Unlike autoregressive LLMs, it does not generate text token by token. Instead, it uses an iterative denoising process. This process is much faster than autoregressive generation. Diffusion LLMs aim to match autoregressive models in performance. At the same time, they offer unique advantages. → They can reason bidirectionally. → They can generate text in any order. → They can also fill in missing text naturally. → You can even finetune BERT into a lightweight chatbot. This uses masked instruction tuning. It shows that encoder-only models can generate text. They do not need to switch to autoregressive architectures. ModernBERT-large-chat reaches 93% of Llama3-1B’s MMLU performance. It does this with 60% fewer parameters. The library provides ready-to-use training pipelines. These support LoRA, DeepSpeed, FSDP, and multi-node distributed training. It also includes unified generators. These simplify inference across different diffusion architectures, like LLaDA, Dream, and BERT. EditFlow extends diffusion models with edit operations. It can insert, delete, or substitute text. This allows flexible non-autoregressive generation. It also handles variable-length sequences. Its position-relative text manipulation works better than standard masking approaches. Diffusion LLMs are not yet as high-quality as autoregressive models. However, they have made a lot of progress this year. This makes it an exciting time to explore them. The repo includes neat examples. These examples act as step-by-step tutorials for beginners. ♻️ Follow me for more AI. 🎗️ Don't forget to check out the comments section for the repo link.