Build-Out-Loud" is a channel for sharing what you're building with Workato in real time: work-in-progress recipes, demos, prototypes, and lessons learned as you build, so others can follow along, give feedback, and learn from your process.
Share what you're building!
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Hey everyone! 👋 A little update on Verirow and what I’ve been working on lately. I’m building Verirow to help teams check whether data actually arrived correctly after an integration runs, even when the integration itself reports success. Over the past couple of weeks, I’ve been lucky enough to get some really helpful feedback from dheeraj k. Dheeraj here in the community. Huge thanks to him for spending his time testing Verirow and pointing out things I hadn’t considered! 🙏 His feedback led to quite a few improvements:
Clearer explanations of where expected values come from, including field mappings and transformations.
Better handling of sync delays and dependencies. For example, if a record needs approved = true before syncing, Verirow can keep it in WAITING instead of flagging it as missing too early.
More useful run history, object context, and timezone settings.
Clearer results and easier investigation of mismatches.
I’ve also been working on Workato recipe import, so Verirow can read mappings and formulas from recipes instead of requiring everything to be configured manually. I finally set up my own Workato test workspace to start checking this against the real API. This part is still in beta, and I’m working through the connection flow. If anyone here works with Workato and would like to help test the integration using a sandbox or test workspace, I’d genuinely love to hear from you. No production access needed, and the beta is free. I’m also curious how other teams verify their data after recipes run successfully. Do you use separate reconciliation checks, or mostly rely on the integration’s own monitoring? And thanks again to dheeraj k. Dheeraj for being so generous with his time. It’s made a real difference in how Verirow is developing! 😊 verirow.com/workato
Genesis lets your existing coding agent build custom backends from structured application blueprints. Those services can then connect to Workato through APIs, combining custom application development with enterprise integration and orchestration.
Hi everyone! Thanks to Aaron W. for introducing me to this channel. I’m the founder of Mindbricks, and I’d love to invite you to try Genesis, our deterministic compiler for building backends with AI. You can use it directly from your existing coding agent through MCP. Your agent designs and updates a structured application blueprint; Genesis compiles it into backend code, including APIs, databases, permissions and deployment configuration. You keep working in your familiar tools and get real source code you can take with you. I’m offering community members free trial access—especially if you’d like to connect your own agent and build a project through MCP. To get started, register at mindbricks.com, then DM me the email you used. I’ll add you to a trial workspace so you can create a project and start experimenting. Happy to help you get connected, and I’d love your feedback!
Do you use lookups in your recipes like I do? Do you struggle to find what the values actually are? You know, opening lookup tables in a new tab, finding the right one, finding the right row, forgetting why you went there... 🤯 So I had Claude build me a little Chrome extension for it. Click the icon next to a lookup and it shows the current value, plus a “Last run” tab with what it actually was in the latest job. It’s very rough, and honestly it’d be nice if this was a feature in Workato itself. 👀 Anyone else solved this some other way, or have I just been suffering alone?
📚 The TalentXM POC Library is open We've been shipping a lot of agentic builds lately — so we put the proof-of-concepts in one place. If you're exploring AI agents for healthcare RCM, ops, or retail, this is a good place to see what "production-ready" actually looks like. A few of the builds you'll find inside: 🏥 DenialXM — Denial Management Genie — turns one plain-language request into a set of denied claims, each classified, scored for recoverability, and routed to a named owner with a next action. ✅ Eligibility Check Genie — real-time insurance eligibility via the Optum API (X12 270/271), from chat, a Google Sheet batch, or an insurance entry point. 🔄 Patient EngageXM (Early Access · MCP) — ranks dormant patients worth re-engaging from athenaOne data, with a live engagement funnel. 📅 ScheduleXM + Healthcare Scheduling Genie — automate the full appointment lifecycle through Athenahealth EHR: slot discovery, patient registration, and booking. 🛒 Retail Control Plane MCP — a governed MCP server that senses real-time demand exceptions on any style/SKU and recommends network-aware replenishment. All built on Workato (Agent Studio + MCP). Browse the full library here 👉 talentxm.ai/poc-library Always happy to compare notes on agentic patterns, EHR/API integrations, or MCP design — drop a comment or DM. 🙌
Hey Workato community! 👋 The team at TalentXM has just shipped a batch of agents, Genies, and MCP servers to the Marketplace — most of them focused on healthcare RCM and operations, plus one for retail. Would love your feedback if any of these are relevant to your builds: 🏥 DenialXM — Denial Management Genie Turns one plain-language request into a set of denied claims, each classified, scored for recoverability, and routed to a named owner with a next action. ✅ Eligibility Check Genie Verifies patient insurance eligibility in real time via the Optum eligibility API (X12 270/271) — from chat, a Google Sheet batch, or an insurance entry point. 🔄 Patient EngageXM (Early Access · MCP) Turns athenaOne appointment + clinical-workflow data into a ranked, reason-coded list of dormant patients worth re-engaging, with a live engagement funnel. 🛒 Retail Control Plane MCP A governed MCP server that senses real-time demand exceptions on any style/SKU and recommends network-aware chase or pullback replenishment. 📅 ScheduleXM Agent MCP Server AI-powered MCP server automating the full healthcare appointment scheduling lifecycle through Athenahealth EHR — from patient creation onward. 🗓️ Healthcare (Appointment Scheduling) Genie Production-ready agent letting healthcare staff manage the full appointment lifecycle — slot discovery, patient registration, and booking. Everything’s live under TalentXM on the Marketplace. We built these on Workato (Agent Studio + MCP), and we’re always happy to compare notes on agentic patterns, EHR/API integrations, or MCP design. Drop a comment or DM! 🙌 marketplace.workato.com/en talentxm.ai blocktxm.com
Hey everyone! Excited to share a quick Genie hobby build I put together recently: an AI-Powered Workato Resume Evaluator & ATS Coach. The Problem: Navigating iPaaS job requirements can be tricky—job descriptions often list a wide spread of requirements (recipes, custom SDK connectors, API platforms, error handling, governance), and candidates rarely know how well their resume actually translates against an ATS or hiring bar. How It Works: Genie Knowledge Base: Curated a structured repository of real-world Workato and enterprise integration job descriptions (role levels, must-have skills, connector ecosystems, enterprise patterns). Slack Bot Interface: A user submits/pastes their resume or targets a specific JD tier directly inside Slack. Gap Analysis & Scoring (0–10): Genie maps the resume's experience directly against the JD criteria, generating a weighted fit score. Upskilling & ATS Recommendations: Instead of just a raw score, the bot outputs actionable guidance: Specific technical gaps to bridge (e.g., Connector SDK, On-Prem Agent architecture, API platform management). Tailored ATS wording suggestions to better quantify business impact and integration scope. Next Steps / Iterations: Planning to test automated mock scenario interview questions based on the identified gaps. Would love to hear thoughts, edge cases you’d test, or how you’re leveraging Genie for internal enablement! 🙌
I’m building Verirow, a post-sync reconciliation tool. The basic idea is simple: an integration can report success, but the source and target can still be different. Verirow compares both sides and flags missing, different and duplicate records. The part I’m working on now is what happens after a mismatch is found. For people building integrations here: where do you normally get the root cause from? Integration/job logs, rejected records, target validation errors, API responses, or manual investigation? I’m trying to understand the real workflow before I build that part further.
Sharing our Eligibility XM Agent by TalentXM A claim denied for eligibility was usually preventable before the patient even got to the exam room. Front desks still verify insurance by hand. They retype member IDs, toggle between payer portals, and chase coverage details while the waiting room fills up. When something slips, it shows up weeks later as a denial, rework, and lost revenue. So we built Eligibility XM, an AI agent on Workato that verifies patient insurance in seconds through the Optum Eligibility API. It works the way your team already works: 🔹 One patient, right now. Type the patient details in chat and get coverage status back instantly. 🔹 A whole day's schedule. Point it at a spreadsheet of upcoming appointments. It verifies every row, writes results back, and gives you a summary of what needs attention. 🔹 Just the insurance card. Drop a card image into a shared drive and Workato IDP reads it, builds the request, and returns the result. 🔹 Straight from email. Patients or staff email a card photo, and the verification comes back by email. Behind the scenes, Optum handles the X12 270/271 translation. Your staff never touch EDI. They see a clear answer: active, inactive, or needs review. What this changes: ✅ Fewer eligibility-related denials ✅ Less manual rework at registration ✅ Staff time back for patients instead of payer portals This is one piece of a bigger push at TalentXM: building practical, governed AI agents for healthcare revenue cycle, alongside our work on denials, prescription refills, and care gap closure. If eligibility checks are still a manual bottleneck for your organization, I'd love to show you Eligibility XM in action.lnkd.in/p/gSjFXk5G
How We Automated Procurement to Dispatch Across Multiple Locations Using Workato 🚀 One supply chain. Multiple locations. Disconnected systems. One integration orchestration layer — Workato! In a distributed manufacturing and retail environment, procurement, order management, manufacturing, and logistics often operate across different applications and geographical locations. The challenge wasn't just connecting applications. It was ensuring that every business event triggered the right action in the right system, at the right location, without manual intervention. Here's a step-by-step breakdown of our hybrid integration approach using Workato. 🔹 Step 1: Connect the Enterprise Landscape We established hybrid connectivity between cloud and on-premise systems using Workato connectors, APIs, and the On-Prem Agent where required. Integration landscape: * ERP — Procurement and inventory * OMS — Order management and fulfillment * Manufacturing systems — Production scheduling and status * Logistics — Dispatch and shipment tracking Goal: Establish reliable, bidirectional communication without replacing existing applications. 🔹 Step 2: Automate Procurement Events Configured event-driven Workato recipes to initiate procurement workflows. * Capture purchase order creation and updates from ERP. * Validate supplier, SKU, quantity, and delivery location. * Route procurement details to the appropriate systems. * Synchronize supplier acknowledgments and procurement status. Outcome: Reduced manual handoffs and improved procurement visibility. 🔹 Step 3: Orchestrate Order Management Once the required procurement or inventory conditions were satisfied, Workato orchestrated the downstream order workflow. * Validate orders against inventory and fulfillment rules. * Synchronize order details with OMS. * Identify the appropriate fulfillment or manufacturing location. * Update order statuses across connected applications. Outcome: A consistent order lifecycle across systems and locations. 🔹 Step 4: Trigger Manufacturing Notifications This was a critical part of the integration. Workato enabled automated communication between order management and manufacturing teams. * Detect manufacturing-ready orders. * Trigger production notifications for the assigned facility. * Share order quantities, SKUs, priority, and required dates. * Capture production acknowledgments and milestone updates. * Synchronize manufacturing status back to OMS and ERP. Outcome: Better coordination between business operations and manufacturing facilities. 🔹 Step 5: Automate Dispatch Readiness Once manufacturing completion was confirmed: * Validate production completion and fulfillment conditions. * Update order readiness in OMS. * Trigger logistics and dispatch workflows. * Synchronize shipment references and dispatch confirmations. * Notify relevant stakeholders of status changes. Outcome: Faster coordination between manufacturing completion and dispatch. 🔹 Step 6: Build Exception Handling and Recovery Automation without resilience only shifts operational problems from one system to another. We focused on: * Centralized error handling and notifications. * Retry mechanisms for transient failures. * Duplicate-event prevention and idempotency. * Correlation IDs for end-to-end transaction tracing. * Exception escalation when manual intervention was required. Outcome: More reliable processing with improved operational control. 🔹 Step 7: Establish End-to-End Monitoring Used Workato job monitoring and integration-level tracking to improve operational visibility. Tracked key stages including: ✅ Purchase order processing ✅ Order synchronization ✅ Manufacturing acknowledgment ✅ Production completion ✅ Dispatch confirmation ✅ Integration failures and recovery Outcome: Better visibility into the entire procurement-to-dispatch lifecycle. Build Out Loud
WIP: SAP PO Assistant, a Workato Genie on live SAP data We've connected Workato to an SAP S/4HANA sandbox and put a Genie on top so anyone can ask about purchase orders in plain English. The connection - Workato HTTP connector → SAP S/4HANA OData APIs - API key auth against the SAP sandbox - 3 PO skills: purchase orders, PO lines, goods receipts The outcome so far The *SAP PO Assistant* Genie answers questions like: - "What's the status of this PO?" - "Has this order been received?" - "Does this invoice match its PO?" It pulls the answer straight from SAP. No SAP GUI, no transaction codes, no waiting on someone to run a report. What we've learned - Test the OData URL on its own first ($filter, $expand), then build the recipe step around it - Keep the matching logic in a Python step; it's much easier to read and change than chained formulas - Save a few real SAP documents as a regression set and re-run them after every change Next: moving from sandbox to a real S/4 connection (OAuth 2.0, read-only user). Looking for feedback on: has anyone moved a Genie from an SAP sandbox to production S/4? Did you stay on the HTTP connector or switch to the SAP OData connector?
Hi All! I wanted to share this amazing video from our very own Shubham K.: Build Workato Recipes with Codex + Workato CLI | Full Tutorial. • A tutorial like this isn't technically "building out loud" since it's a demo designed to teach, but I think it captures exactly what this channel is about: sharing how you build so others can learn from your process and apply it to their own journey. • There's also something different about learning from content shared by someone who's actually here in the community. Having a person behind the work creates more trust and a more meaningful connection, especially when you can engage directly with the person who created it. • Hope we continue to see more people sharing what they're building, creating, and learning as the Build Out Loud channel grows! Thank you Sukumar P. and evan.yates for your posts!
We have built an order management flow which helped the company bring down the tat from 96–120 hours to just 4–8 hours Previously, an order could take 4–5 days from placement to dispatch, largely because of manual handoffs, batch processing, validations, and updates across multiple systems. With Workato, we automated the journey step by step: 1. 🛒 Order Placed — New orders are captured automatically from the sales/eCommerce channel. 2. 🔍 Validation — Customer, product, pricing, inventory, and mandatory order details are validated in real time. 3. 🔄 Order Creation — Valid orders are automatically pushed into the Order Management/ERP system. 4. 📦 Inventory & Fulfilment — Stock availability and fulfilment information are synchronised across systems. 5. ⚡ Exception Handling — Failed or incomplete orders are identified immediately and routed for action instead of waiting for manual reconciliation. 6. 🚚 Warehouse Dispatch — Fulfilment instructions reach the warehouse automatically and dispatch status flows back to the relevant systems. 7. 📊 End-to-End Visibility— Every order can be monitored through automated logging, alerts, and status updates. The impact: Order-to-dispatch turnaround reduced from 96–120 hours to 4–8 hours.
