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The workflow engine is the execution backbone of ZGI agents. It turns visual node graphs into reliable, production-grade AI pipelines.

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Execution Engine

Workflow execution uses SSE (Server-Sent Events) streaming, delivering each node’s results in real-time. The frontend can display step-by-step progress: 
Node 1 [Start] ✓ → Node 2 [Knowledge Retrieval] ✓ → Node 3 [LLM Analysis] ⏳ → Node 4 [Output]
Each node execution event is pushed to the client, including:
  • Node ID and name
  • Execution status (running / success / failed)
  • Execution duration
  • Output variables and values

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Production-Grade Reliability

ZGI workflows are designed for production, not just demos:
  • Assertions — Define validation rules per node (e.g., “retrieval must return citations”, “output must match JSON schema”)
  • Retry — Failed nodes auto-retry with configurable limits and backoff
  • Fallback — When retries exhaust, degrade gracefully (e.g., output a “pending review” list instead of failing silently)

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Workflow Lifecycle

  1. Draft — Edit and save on the visual canvas. Auto-saved every 30 seconds.
  2. Debug — Test with sample inputs. SSE streams node-by-node execution in the debug panel.
  3. Publish — One-click publish generates a version number and makes the workflow available via API or chat.
  4. Monitor — View run history, execution logs, token consumption, and error rates.

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Example: Contract Review Workflow

[Start: Upload Contract]
    → [OCR Parse Document]
    → [Knowledge Retrieval: Legal Clauses]
    → [LLM: Risk Analysis]
    → [Conditional Branch: Risk Level]
        ├── High Risk → [Notify Legal Team via Webhook]
        └── Low Risk → [Generate Report]
    → [End: Return Structured Report with Citations]