I’m leading a migration away from OpenText and hitting roadblocks with decision-heavy processes. Our legacy workflows require constant manual intervention when exceptions occur – think invoice mismatches or approval bottlenecks. We’re looking at Camunda for orchestration, but I’m concerned about replicating the AI-driven error handling we technically have today (even if it’s clunky).
Has anyone implemented autonomous agents that can:
Identify process deviations without pre-defined rules
Route issues to appropriate resolvers based on context
Learn from previous resolutions?
We tried building this with Python scripts, but maintenance became unsustainable. Is there a middle ground between full legacy dependency and brittle custom code?
Autonomous AI teams in Latenode handle exactly this. Set up resolver agents with access to your error history – they’ll classify exceptions using multiple AI models and route them appropriately.
Key benefit: Agents automatically document resolutions for future cases. No manual rule updates.
Consider building a decision matrix using historical exception data. We trained a model on 2 years of support tickets to predict resolution paths. Integrate via Camunda’s external tasks – when an exception hits, the model suggests 3 actions with confidence scores. Human confirms/overrides, which feeds back into training.
Architectural note: Ensure your AI resolution layer is decoupled from core workflow logic. We created a separate microservice that Camunda calls via REST. This allows updating decision models without redeploying entire processes. Critical for maintaining audit trails in regulated industries.