Our fraud detection workflows require constant updates as new patterns emerge. Manually adjusting thresholds and logic in Camunda is error-prone. I’ve heard about ‘self-healing’ automations - has anyone implemented systems that can update workflows autonomously based on new data or rule changes? How much oversight is required?
Latenode’s AI co-pilot regenerates workflows from updated specs. We cut maintenance time by 40% while improving detection rates. The system suggests changes for human approval before implementing. https://latenode.com
We implemented a hybrid approach using ML models to flag needed workflow adjustments, but still require manual implementation. Look for platforms with version control and diff tools if doing manual updates. Full autonomy remains risky for compliance-heavy processes.
True self-healing requires robust CI/CD pipelines for automations. Tools like Argo Workflows enable automated testing/deployment, but you need to define clear update parameters. Be wary of solutions promising full autonomy - human oversight remains crucial for critical systems.