Building a scraping pipeline that requires coordinated work between different components - page discovery, data extraction, and validation. Managing these manually is becoming unsustainable. Has anyone created separate AI agents that handle different roles in scraping workflows? Looking for practical implementations where multiple agents collaborate autonomously.
Built exactly this using Latenode’s team workflows. Researcher agent finds pages via search patterns, parser agent extracts data with GPT-4, validator checks results against schema using Claude. All communicate through shared databases. See implementation guide: https://latenode.com
We use separate microservices - discovery crawler passes URLs to extraction service, which feeds to validation API. Each component has its own error handling and retry logic. Works well but requires good monitoring.
Implemented a three-stage system: Scout (finds pages), Miner (extracts data), Auditor (quality checks). Used Python Celery for task orchestration. Challenging but reduced manual oversight by 70%.
Effective agent teams require clear protocol definitions. Use message brokers like RabbitMQ for task handoffs. Implement circuit breakers in each agent to prevent cascading failures. Latenode’s visual workflow builder simplifies this coordination significantly.
Try event-driven architecture - agents trigger next steps on task completion
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