Big news: StackAI is joining @asana!  We created StackAI to empower enterprises with secure, powerful agentic workflows that automate manual processes; Asana created the operating system for human-agent teams.  Now, we're building the place where humans and AI agents execute across every system a business runs on. Same product, same team, same brand, and way more fuel. We're just getting started. 🚀

I’m thrilled to share that StackAI is joining forces with Asana. From the moment we met Dan Rogers, Asana’s CEO, Arnab Bose, Asana’s CPO, and the rest of the team, it was clear they deeply understood what makes StackAI different: how we build, who we serve, and why it matters. When we started StackAI in early 2023, we believed LLMs would fundamentally redefine how work gets done. Today, with the rise of AI agents, that future is arriving faster than anyone expected; and we believe we’re uniquely positioned to help shape it. Over the past few years, companies across a wide range of industries have adopted StackAI to build reliable AI workflows for critical business operations. That momentum exists because of an extraordinary team that has obsessed over every detail of the product experience: from workflow reliability and orchestration to scalability, governance, and usability. At the same time, Asana has spent nearly two decades building one of the most trusted platforms for work management, used by 85% of the Fortune 100 and loved by millions of users worldwide. What makes this partnership especially exciting is our shared belief that the future of work will be built around seamless collaboration between humans and AI agents. Together, we have an opportunity to build something entirely new: a true operating system for human-agent execution across teams, tools, and data. I’ll continue leading the StackAI team alongside my co-founder Bernard and our incredible team, reporting directly to Arnab Bose. Arnab brings a rare combination of ambition, humility, and product leadership, and I couldn’t imagine a better partner for this next chapter. Importantly, StackAI remains StackAI: same team, same product, same brand, same commitment to customers. What changes is our ability to move faster, scale further, and bring more powerful AI capabilities to organizations around the world as part of Asana. This is not just about making individuals more productive. It’s about enabling entire organizations to operate differently: with humans and AI agents working side by side through governed, reliable workflows. To our customers: thank you for trusting us early. We plan to keep shipping faster than ever. To our team: this moment belongs to you. Your craftsmanship, intensity, resilience, and ambition made all of this possible. Now we get to build at an entirely different scale. Excited for what’s ahead, let’s keep cooking!

One question I hear constantly while filming inside AI startups is: what happens when OpenAI just builds this? While filming INSIDE @stackai, co-founder @BernAceituno gave a pretty compelling answer. StackAI has raised $16.6M to build an enterprise AI agent platform that lets organisations automate complex workflows without writing code. For hospitals, defense contractors, and financial institutions, AI infrastructure has to meet strict compliance requirements. Once those workflows are embedded into an organisation, the switching cost isn’t the model, it’s everything built on top of it. The companies that survive probably won’t just be the ones with the best models. They’ll be the ones that enterprises cannot remove. Their INSIDE Startups episode crossed 30,000 views within 48 hours and drove over 500 high-intent clicks through to their website from YouTube alone. It's well past that now. Full episode link in comments!

Proud to partner with @Egnyte to accelerate agentic workflows for document-heavy processes⚡️ Excited for the use cases across financial services, construction, engineering, and more that this integration will unlock.

.@BernAceituno wrote about the mistakes CIOs make all too often, and how to avoid them. Read the full article below at CIO.com!

For a company automating enterprise workflows, @StackAI couldn’t let their website become a bottleneck. After building their site in Framer in just two weeks, their lean team now ships site updates daily and has scaled to nearly 2,000 pages, all without relying on developers.

Everyone go home, @stackai just achieved AGI… Just kidding ;) But this is getting very close. Agentic Workflows... 100x more powerful.

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/weav…

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/weav…

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

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 ⚡️