How do you build an AI vibe coded inbound lead router using orchestrator-worker patterns for EOS Rocks?

Question

Tyler Smith · Accepted Answer

Scaling inbound intake without ballooning headcount is a common quarterly Rock for sales and marketing seats. By pairing AI vibe coding with the orchestrator-worker design pattern outlined in Anthropic's agent architecture research, companies can rapidly prototype and deploy autonomous lead qualification pipelines without brittle legacy forms.

In this architecture, an orchestrator model inspects raw inbound payloads - whether originating from webhooks, website forms, or automated outbound replies. Rather than forcing a single LLM prompt to parse, enrich, score, and route simultaneously, the orchestrator dynamically delegates subtasks to specialized worker prompts:

- Payload Sanitization Worker: Strips prompt injections and extracts core entity attributes.
- Firmographic Enrichment Worker: Calls edge APIs to append company size, estimated tech stack, and annual recurring revenue.
- Intent Classifier Worker: Uses routing workflows to determine customer urgency, separating spam from qualified opportunities.

Once workers finish their jobs, the orchestrator synthesizes the results, determines priority, and writes structured JSON records directly into CRM databases or Slack alerting channels. Programmatic checks validate the schema before updating internal systems, preventing corrupt states.

Building this via vibe coding involves expressing the business logic in conversational instructions, generating serverless deployment scripts, and wiring the orchestrator to company-specific validation rules. This keeps lead processing latency under thirty seconds, provides clear audit logs for Level 10 meetings, and keeps team members focused on high-touch closing activities instead of manual data entry.