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Ramón Cortez
Ramón Cortez

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Building Resilient Low-Code Agent Pipelines with Automated Fallbacks

#mlhacks #devchallenge #hackathon

This is a submission for the MLH x DEV Writing Challenge

What I Built

I built an autonomous lead intake and qualification pipeline designed to solve silent failures in low-code agent workflows. When running multi-step agent routing, malformed JSON payloads or API limits often cause execution passes to fail without recovery.

This architecture enforces strict JSON schema validation at the routing tier and implements a zero-downtime execution fallback, routing flagged or failing payloads into a manual review queue while preserving system state.

Demo

Partner Technologies

  • Relevance AI: Multi-agent autonomous workflow orchestration and agent execution engine.
  • Google Sheets API: Dynamic lead data logging and state tracking.
  • Python: Custom state routing logic and schema assertion harnesses.

How I Built It

  1. Webhook Ingestion: Automated lead intake captures incoming data directly from client forms.
  2. State Routing Engine: Standardized routing logic evaluates account attributes against predefined priority tier rules.
  3. Fallback Guardrails: Wrapped core execution steps in explicit execution handlers so API timeouts divert leads to a manual review status rather than breaking the pipeline.

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