StackAI

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 👇

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.

Multi-Agent Teams are live on StackAI. 🎉 One agent doing everything is a ceiling. So StackAI built the alternative: 1️⃣ An Orchestration System breaks a complex request into specific jobs 2️⃣ Sub-agent specialists execute them in parallel- own instructions, own tools, own knowledge bases 3️⃣ The Orchestration System synthesizes everything into a single, highly accurate output Any workflow you've already built attaches as a Subflow Tool in the StackAI platform and becomes a specialist agent, nothing to rebuild from scratch. f.mtr.cool/t3othae6u5

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