Our document indexing errors dropped from ~20% to below 0.1% after we moved to Temporal Technologies. Before an agent can use a customer's documents, our pipeline has to parse and index every file. On our old setup, when one step failed, the retry started over from the beginning, including paid steps that had already worked. So we moved the pipeline to Temporal incrementally. 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. Our CTO Frank Hidalgo and AI engineer Gabriel Mermelstein wrote up how we did it 👇 stackai.com/blog/how-stackai…

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 → stackai.com/blog/stackai-giv… #EnterpriseAI #AIAgents #AgenticAI

Introducing Custom Actions. Build the exact action your workflow needs, on the apps you already use. StackAI's native actions cover the common operations: create a record, send a message, look up a row. But the steps that matter most, like setting a priority or applying a naming convention, are unique to how your business works, and they're usually what's missing. Now you can build it yourself, just by describing what you need. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀|  - 𝗗𝗲𝘀𝗰𝗿𝗶𝗯𝗲 𝗶𝘁: pick the app and tell the StackAI agent what the action should do. It reads the app's API docs and writes the schema and code for you.  - 𝗧𝗲𝘀𝘁 𝗶𝘁: run it against a real connection before any workflow depends on it. - 𝗦𝗵𝗶𝗽 𝗶𝘁: it appears in the builder and every agent's tool list, right next to native actions. 🔒Actions run sandboxed on connections your admins already manage, and each saved version is locked, so IT stays in control and an edit never breaks what's already running.

One example of what Multi-Agent Teams on @stackai can do: a weekly SEO report. ✨ You build one agent and ask it for the report. It works out that the job needs: - traffic and behavior from Google Analytics - rankings and impressions from Search Console - backlinks and competitive position from Ahrefs - strategy and history from a knowledge base The agent creates a sub-agent for each, runs them in parallel, and combines the results into one report.  This is the simplest version of it. The same agent handles claims review, diligence, or RFP responses, and it goes well beyond a single report. Build one agent. It executes the rest.

👀 GPT-6 Astra is coming to @stackai  – and here's why you'd want to build your next AI agent with it: It's OpenAI's most capable model, built for the hardest end-to-end work – with a 1,050,000-token context window, 128,000 max output tokens, and top-tier reasoning. It's state-of-the-art on computer use, browsing, and software engineering. For enterprise teams, that means dependable execution on tedious multi-step workflows that used to require a human at the keyboard... Filling out spreadsheets, updating customer records + stronger performance across coding, research, and document generation, backed by native support for code interpreter, file search, and web search tools. Put GPT-6 Astra to work on StackAI and automate your most critical back-office processes with speed, scale, and control 🚀

A property manager was drowning in work orders. Now, with @stackai, when a tenant emails "My HVAC died last night": 1. GPT-5.6 reads the email and pulls the details: tenant, unit, issue, urgency. 2. Claude queries the vendor directory and matches the right HVAC pro. 3. Claude pings the team on Slack to approve ✅ or reject ❌ (human-in-the-loop) Once approved: emails the vendor the full work order, confirms with the tenant, and updates the system of record TL;DR: 💰 ~$400K saved annually in admin labor ⚡ Faster response on urgent issues 📥 Zero work orders lost in the inbox

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. 🚀

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

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