StackAI

We’re excited to introduce subagents on StackAI 🚀 Computer use gives AI a workstation. Browser use gives it reach across the web. Multi-agent orchestration turns AI into a team – with a manager and specialized subagents. The manager/orchestrator AI agent receives a high-level goal, breaks it into tasks, and delegates to specialists – each with their own tools, context, and expertise. Then the manager reviews, reconciles, and delivers a single polished output. Why it matters? ⚡ Speed: the manager runs multiple subagents in parallel, not one at a time. 🧠 Focus: each subagent specializes in a specific task. 📈 Scalability: complex problems naturally break into small, focused teams of agents. Watch our new video and book a demo to see subagents in action👇 #StackAI #Subagents #EnterpriseAI

We at StackAI got early access to GPT-5.4 — and the results are remarkable. One of our benchmarks (analyzing financial records across 50,000 pages) had never been passed by any LLM. GPT-5.4 is the first to clear it. Impressive work, OpenAI 😮

We’re excited to welcome StackAI as a Tech Tale Sponsor for #CIOCISOXNewYork! 🚀 Stack AI empowers organisations to build and deploy AI-powered workflows that transform how teams operate. By enabling businesses to integrate advanced AI capabilities into everyday processes, Stack AI helps companies automate tasks, unlock insights from data, and accelerate innovation across the enterprise. Join us at #CIOCISOXNewYork - the premier gathering for forward-thinking technology and security leaders shaping the digital future across the United States. Together, we’ll exchange strategic insights, build meaningful connections, and explore the technologies driving the next era of enterprise transformation. 📅 Save the date: 14 April 2026 📍 Well& by Durst, One Five One, New York RSVP for free: 🔹 CIO Track: https://lnkd.in/eKuAawBX 🔹 CISO Track: https://lnkd.in/eHyisnfp 💡 Interested in sponsorship opportunities? Contact sponsorship@edsxevents.com #WelcomeWednesday #StackAI #EDS #Xseries #CIOCISOXNY #ArtificialIntelligence #EnterpriseAI #Leadership #Innovation

95% of AI agent demos never make it to production. Yet 79% of enterprises expect full-scale agentic AI adoption within three years. So what's the disconnect? Most companies jump into AI agents without understanding what makes them fail at scale. The gap between demo and production is massive. We’ve created this free guide with StackAI and Weaviate that breaks down exactly what goes wrong: 𝟭. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Why agents leak data without proper access controls 𝟮. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: How poor RAG implementation causes hallucinations 𝟯. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 𝗮𝗻𝗱 𝗲𝘃𝗮𝗹𝘀: The protection mechanisms that keep agents reliable 𝟰. 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: Why complexity grows nonlinearly with multi-agent systems Plus, real-world use cases showing how to build production-grade agentic RAG systems. Get your free copy here 💚 https://lnkd.in/enriGj6W

Join us next week to discuss the future of agentic AI for manufacturing!

Think RAG is just vector search and retrieval? It's actually 7+ different architectures (you might be using the wrong one) 1️⃣ 𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 - The Vanilla approach. Documents get chunked, embedded, and stored in a vector database. When a query comes in, you retrieve the most similar chunks and pass them to the LLM. 2️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗲-𝗮𝗻𝗱-𝗥𝗲𝗿𝗮𝗻𝗸 - Naive RAG + a crucial step: after initial retrieval, a reranker model re-scores and reorders the results for actual relevance. This catches cases where semantic similarity doesn't perfectly align with what the user actually needs. 3️⃣ 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 - Handles more than just text. Images, videos, audio - this architecture uses multimodal embedding models to encode different data types into the same vector space, then retrieves and generates responses across modalities. 4️⃣ 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚 - Instead of treating documents as isolated chunks, this approach builds a knowledge graph that captures relationships between entities and concepts. 5️⃣ 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚 - Combines Vector Search with Graph RAG. By combining semantic retrieval with structured relationship mapping, you get a system that understands both the "what" (intent) and the "how" (connectivity) of your data. 6️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (𝗥𝗼𝘂𝘁𝗲𝗿) - Instead of a single retrieval path, an AI agent decides which search engine or knowledge source to query based on the user's question. It might hit a vector database for one query, a web search for another, or multiple sources and combine them intelligently. 7️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 (𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗥𝗔𝗚) - The most sophisticated. Multiple specialized agents work together, each with access to different tools and databases. One agent might search internal docs, another queries external APIs, a third handles web search - all coordinating to answer complex queries that require information from multiple domains. The architectures get progressively more powerful but also more complex to implement and maintain. Start simple, then level up as your use case demands it. This was just a peek into StackAI and Weaviate latest ebook about building production-grade agentic RAG systems, get your free copy here: https://lnkd.in/enriGj6W