I think you meant premises.
previously writing RPA code used to take a long time - using AI (and its infinite patience) we can write more durable code that covers more edge cases
And since they’re code based it’s pretty straightforward to an agents monitor them and update their code when upgrades to the underlying system happen etc…
for observability - we have workflow execution logs that store text, videos and screenshots so an agent or a human can debug them - lots and lots of webhooks when things break ! (:
1. RPA code breaks (ex: throws an exception if a window does not exist) 2. RPA reports success but was clicking / typing in the wrong place 3. Underlying system breaks (virtual machine / legacy software)
the skill we have in our MCP is to build the RPA code to throw exceptions where possible so an LLM can understand the context and recover
to avoid false success states we add LLM vision steps in the workflow itself to error out if it sees that the system is in the wrong state
and for the underlying system breaking it can be as simple as having a CRON job that checks the status of the process / the health of the VM and running a script to reboot the system
it depends on the system but the pattern we've seen with RPAs is you can catch maybe 80% of the edge cases in the first week it's been rolled out
I'm not suggesting that you correct your customers, but there's no reason to sink to the lowest common denominator when writing.
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When a UI changes or an unexpected dialog appears, the agent catches it, adapts, and keeps going. A reflection agent verifies every action against what's on screen and self-corrects before the workflow breaks. No more rebuilding scripts every time a vendor ships an update.
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Minicor is a platform for building and running desktop automations at scale with computer use agents. AI companies selling into healthcare, automotive, logistics, and financial services need to read and write to their customers' systems of record. These are old desktop applications with no APIs. Minicor automates them reliably so companies can go live in weeks.
Most legacy desktop systems like EHRs, ERPs, DMS, and PMS have no writable API and never will. Some vendors are actively restricting third-party API access. The only way to read or write data is to interact with the desktop like a human would. That is the problem Minicor solves.
Building one RPA is not hard. Running hundreds reliably is. UIs change, edge cases pile up, errors cascade. At scale, even small error rates become catastrophic. Engineering teams end up spending all their time maintaining automations instead of building their core product.
Traditional RPA vendors use brittle scripts that break when UIs change. Computer use models work for demos but fall apart in production at 80-85% accuracy because they figure everything out from scratch every time. Minicor stores automation as deterministic code and uses the agent only for recovery and edge cases. Faster and more accurate in production.
Minicor self-heals. A reflection agent verifies every action against what is on screen and self-corrects before mistakes cascade. When a vendor ships a UI update, the automation adapts instead of crashing. 93-96% click accuracy vs 80-85% for other approaches.
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Any legacy desktop or web application. Healthcare EMRs like Athena, Epic, Cerner, and PS Suite. Dental PMS like Open Dental and Dental Vision. Automotive DMS like CDK Global. Supply chain systems like SAP and HighJump. Home health systems like Wellsky and Home Care HomeBase. Financial services platforms for claims, banking, and underwriting.