StackAI

Enterprise AI Agents are here 🤖 🏢 Stack AI users are already automating tasks such as InfoSec questionnaires, RFP responses, lead qualification, sales training, and enablement.

Get a demo here: stackai.com/demo?utm_sourc…

We’re excited to introduce subagents on StackAI 🚀 The manager/orchestrator AI agent breaks a high-level goal into tasks, delegates to specialist subagents – each with their own tools, context, and expertise – then reviews & delivers one polished result. Why it matters? ⚡ Speed: tasks run in parallel, not sequentially 🧠 Focus: each subagent handles a specific task 📈 Scalability: complex problems break into small, focused teams Combine this with computer + browser use ➡️ an enterprise-ready AI team with a manager, specialists, and built-in quality control. Watch our new video & 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 😮

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_io 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 💚 stack-ai.com/whitepaper/wea…

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_io latest ebook about building production-grade agentic RAG systems, get your free copy here: stack-ai.com/whitepaper/wea…

@RosinolToni Way to go!

let's go!

See the full list: agentconference.com/agenticlist/20…

Proud to be named in The Agentic List 2026, which recognizes the top agentic AI companies most admired by enterprise leaders. StackAI makes it easy for teams to turn processes into AI agents in minutes. With a no-code workflow builder, a robust governance and ADLC suite, and white-glove support from experts, we're proud to be the the trusted AI transformation platform for enterprises around the world.  #TheAgenticList2026 #StackAI

You've heard of SDLC. Now, meet ADLC: the framework for scaling AI across the whole enterprise.  As organizations deploy more AI agents, they're hitting the same challenges software teams solved decades ago: How do you prevent unauthorized changes? Track what changed when? Test before production? The Agentic Development Life Cycle (ADLC) brings proven software development discipline to AI agents through three layers: 🔹 Environments – Safe separation of dev, staging, and production 🔹 Version Control – Automatic versioning, diffs, and rollback 🔹 Approval Workflows – Pull requests with admin review Without ADLC, untested changes reach production without audit trails. With ADLC, clear governance and deployment visibility give enterprises the confidence to scale from pilots to production systems.  Read the full whitepaper here: stack-ai.com/whitepaper/adlc

We'll cover: - Why decentralized AI is winning in factory and procurement environments - What operational AI agents look like in manufacturing today (live demos included) - The organizational capabilities that predict whether your AI deployment succeeds or stalls If you're a CIO, head of digital transformation, or quality/compliance leader in manufacturing, this one is for you. Link to register here: luma.com/i2i7s2um

Manufacturing companies need to answer one question in 2026: how do you move from pilots and proofs-of-concept to AI agents that run in production and deliver measurable business impact?  On March 4th, we're hosting a webinar with Fabien Cros (former Data & AI Country Lead for Manufacturing at Google Cloud, now CDAIO at Ducker Carlisle) to talk about what it actually takes to cross that gap.

Introducing Agent Grid 🛍️  All of your agents in one place: organized, standardized, and accessible. Agent Grid makes it easy to scale AI agents across the entire enterprise. Now on StackAI ⚡️

Discover the full partnership story here. stack-ai.com/customers/from…

When Fabien Cros joined Ducker Carlisle from Google, he expected to find teams already building sophisticated AI workflows with frameworks like LangChain and AutoGen. Instead, he discovered something far more common: organizations overwhelmed by AI possibilities, unsure where to start, and relying on small technical teams that had become impossible bottlenecks. But with StackAI, Ducker Carlisle has empowered 100+ business users to build AI agents themselves, supported by a small team of experts providing strategic guidance, achieving what centralized development never could: rapid innovation, $1 million in projected annual savings, and a sustainable model for ongoing AI adoption.

Sharing document ingestion war stories in a webinar with our friends at @llama_index next week. We'll cover:  • How to processes millions of documents across finance, insurance, and legal use cases • Why document parsing and retrieval are critical to agent performance • Lessons learned deploying document-driven agents in enterprise environments, and more  Look forward to seeing you there! Register below: landing.llamaindex.ai/webinar-stacka…

@Cdpetty ♥️🚀

@stackai doesn't just talk the talk, they walk the walk.

@BernAceituno The future of AI in Enterprise! 🔐

@cerebras Read the full partnership story here: cerebras.ai/blog/stackai-c…

We're excited to share more about our partnership with @cerebras, bringing faster inference options to StackAI customers. With Cerebras available on the platform, workflows that need real-time performance get noticeably faster responses—especially helpful for multi-step agent workflows that make dozens of LLM calls. For teams running AI agents at scale, this means more responsive experiences and higher throughput when speed matters. #StackAI #Cerebras #AIagents #Orchestration