StackAI

StackAI is heading to Fort Huachuca! StackAI will join Diné Development Corporation at Fort Huachuca's Innovation Day, hosted by National Conference Services, Inc. (NCSI). The event brings government and industry together to match mission needs with new technology. Stop by our exhibit to: → See how teams build AI agents into complex workflows → Securely connect agents to the systems you already use → Explore use cases like automating ATO documentation → Learn about deployment, audit-ready compliance and human controls 📍 Murr Community Center, Fort Huachuca, AZ 🗓️ Tuesday, November 3, 8:30am to 1:00pm MT Details and registration in the comments. 👇 #FortHuachuca #InnovationDay #AgenticAI #GovTech

𝗝𝘂𝘀𝘁 𝘀𝗵𝗶𝗽𝗽𝗲𝗱: StackAI's Self-Healing Workflows. Higher-quality agent outputs, without hours of debugging. Usually, when an agent fails, someone has to dig through the runs, find the cause and fix the workflow by hand. Self-Healing Workflows take that over and turn it into a simple approval process. When an agent's quality slips, StackAI finds what went wrong, tests a fix and sends it to your team to approve in one click. You define what the output looks like and with every run, StackAI scores the agent's result against it. When a score falls below its threshold, self-repairing starts automatically. Then, StackAI: 1. Pulls out the failed cases and explains the cause 2. Proposes a fix in plain language 3. Tests the fix on a draft workflow, not in production, and reruns the evaluation so you can compare scores before and after Self-Healing Workflows send suggestions automatically, but approval always stays with your team. The result is less time spent debugging and higher quality outcomes. J.J. Miller, our product lead, gives it a quick breakdown below 👇

StackAI's deployment team just got bigger. Meet the 3 newest forward deployed engineers. 👋 👋 Yixin(Annie) Liu is based in San Francisco. She recently finished a Master's in Business Analytics at MIT, after studying Applied Mathematics and Economics at UC San Diego. She's excited to help businesses turn innovative ideas into real-world solutions. 👋 👋 Brian Mann is based in New York. He just finished a Master's in Data Science at NYU, and before that he was a data engineer at a healthcare analytics company. Outside of work, you'll find him running, traveling and exploring NYC. 👋 👋 Nathan Wangidjaja recently finished a Master's in Business Analytics at MIT, after studying Computer Science at Columbia University. He's excited to help businesses turn AI into tools they actually use. Welcome to the team, Annie, Brian and Nathan! #EnterpriseAI #AgenticAI #Hiring

StackAI just hit 500+ integrations 🎉 That means your AI agents can work with the systems your teams already use, like Google Drive, Notion, Slack, Asana, Microsoft 365, Jira and AWS. The team has been working toward this for a while, and we're excited to finally share it. See every integration 👇 https://lnkd.in/emm6Z5Uc

"AGI, for the purpose of enterprise automation, arrived in Feb 2026..." - Bernard Aceituno, co-founder of StackAI (now part of the Asana team). Reason being, he says, is that agents crossed the line of human-level reliability in five main ways: Understanding unstructured data, querying unstructured data, orchestrating tools and actions in a non-deterministic way, interacting with visual interfaces and, synthesizing information across documents and tools. (Also.. admiring the MTS Live team for the fast, direct approach to handing out the news on AI.) https://lnkd.in/gBay7mTF

StackAI's document indexing errors dropped from ~20% to below 0.1% after moving to Temporal Technologies. Before an agent can use a customer's documents, the StackAI pipeline has to parse and index every file. On the old setup, when one step failed, the retry started over from the beginning, including paid steps that had already worked. So StackAI's engineering team moved the pipeline to Temporal piece by piece. Now each stage remembers where it left off, and a retry only re-runs the step that failed. Parsing costs are down to about a tenth of what they were. And customer complaints about indexing have stopped. Read the full story here 👇 https://lnkd.in/ecWcP9hD

Complex AI use cases don’t fail at the build stage. They fail at integration, security and deployment. Most platforms hand you a builder and leave the rest to your team. StackAI pairs every customer with dedicated experts: → An AI strategist who finds use cases where agents pay off and tracks the ROI → A forward-deployed engineer who builds custom integrations, deploys on-premise and trains your team → Support in Slack, Teams or email, plus live calls when you need them It starts with a structured proof of value: three use cases in production on the StackAI platform, running on your data and systems. Learn more on our blog → https://lnkd.in/eWQtgJCV #EnterpriseAI #AIAgents #AgenticAI

StackAI cut its indexing failure rate from about 20% to below 0.1%, and daily customer complaints about indexing stopped. In this case study, Frank Hidalgo, PhD, CTO of StackAI, shares how Temporal helps the team retry only failed steps, trace failures more easily, and scale different parts of the indexing pipeline independently. Read more: https://lnkd.in/dS3F2-wr

Every company has one system nobody is allowed to touch. Mike set it up in 2009. Only he knows how it works. Every AI plan runs into it. A platform alone won't fix that. Someone has to go deep on Mike's server and build the connection. Most enterprises need both to get AI into production. That's what StackAI was built for. The platform: → A no-code builder, so your own teams can build and maintain agents → Connects to the systems you already use, in the cloud or on-prem (Mike's server too) → Humans approve key actions before agents act The team behind it: → An AI strategist who finds where agents pay off and tracks the ROI → A forward-deployed engineer who builds custom integrations and trains your team It starts with a structured 3–6 month deployment: three use cases live in production on StackAI, running on your own data. From there, your team keeps building. Mike finally gets a vacation. 👉 Try it here: https://lnkd.in/g_HNXs_6 What's the oldest system your AI has to talk to? #AIAgents