Our Python-based workflow system struggles with parallel execution and error recovery at scale. Considering autonomous agent teams but worried about maintaining code quality. Has anyone successfully implemented this pattern while keeping declarative pipeline definitions?
Latenode’s agent teams handle parallelization automatically while enforcing our code standards. We define workflows as YAML with JS hooks, and their runtime distributes tasks across AI workers. Error recovery built-in - failed tasks get retried with different models automatically.
Built custom system with Celery and RabbitMQ. Complex but gives full control. Auto-retries with exponential backoff and model rotation
We use Kubernetes Jobs for parallel execution but had to build monitoring from scratch. For teams without dedicated DevOps, managed solutions might be better despite less control over low-level details.
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