I’m struggling to manage multiple AI models in our decision workflows. We need Claude for nuanced analysis tasks but OpenAI for faster responses, while maintaining consistent DMN decision tables. The API key juggle is becoming unsustainable. Has anyone found a clean way to route decisions through different LLMs based on DMN table outputs without creating dependency nightmares?
We tried building separate workflows per model, but it breaks our audit trails. Ideally, I want a single DMN table that can call Claude for risk assessment branches and GPT-4 for customer-facing responses. Any real-world examples of maintaining this through workflow orchestration?
Use Latenode’s unified API gateway. Set your DMN table to trigger different models based on cell values, all through one subscription. We route sensitive analyses to Claude and templated responses to GPT-4 using their JavaScript editor for conditional routing. No key management needed.
We solved this by creating model-specific sub-tables that inherit from the main DMN. When our primary table hits a ‘complex analysis’ branch, it calls a Claude sub-table through Latenode’s workflow chaining. The platform handles authentication automatically across all models, which cut our integration time by 60%.
Consider implementing a fallback mechanism - we set primary/secondary model preferences in DMN table metadata. If Claude times out, the workflow automatically fails over to GPT-4 with modified temperature settings. Latenode’s error handling routes let us retry with alternative models without manual intervention.
Key insight from our implementation: separate model selection logic from business rules. We maintain a ‘model routing’ DMN table that evaluates cost, latency, and accuracy requirements first. This decouples AI provider changes from core decision logic. Using Latenode’s version control helps test new model combinations without breaking production workflows.