Most AI projects start with a simple prototype. Hardcode a prompt, call one model, hope for the best. That works fine for a demo — but it doesn't scale.
Prompts drift without versioning. Models get swapped manually across codebases. Costs balloon when no one's tracking token budgets. And when a provider has an outage? The whole system goes down.
We built an AI workflow platform to solve these problems at the infrastructure level. Instead of treating prompts and models as throwaway strings or config files, we treat them as first-class infrastructure — versioned, auditable, and safe to use in production.
Prompts as Infrastructure
In our system, prompts aren't just text blobs. They're versioned artifacts with:
- Immutable versions stored securely (inline or S3 + KMS)
- Pointers that define the active version — with instant rollback if something goes wrong
- Audit trails so you always know who changed what, when, and why
Need to adjust instructions for a workflow? Deploy a new version, test it, and roll back instantly if needed. No redeploying code. No blind edits in production.
Models as Infrastructure
Models live in a centralized Model Registry. Each entry defines:
- Capabilities: streaming, JSON mode, function calling, multimodal input
- Limits: context windows, reserved tokens
- Costs: per 1K tokens (input + output)
This means you can experiment safely:
- Switch between models without touching code
- Try smaller, cheaper models and see if the results hold up
- Protect uptime with circuit breakers that trip if a model starts failing, preventing cascading outages
Node-Based Workflow Engine
On top of this, everything runs through a graph-based workflow engine. Workflows are built as nodes and edges — ModelInvoke, VectorSearch, Router, Memory, SlotTracker, and more.
This makes pipelines:
- Composable: chain nodes together for complex behavior
- Maintainable: validate every workflow before deployment (no dangling edges, no dead ends)
- Explainable: anyone can see how information flows from user input to model output
The node architecture gives you flexibility: run simple chatbots, retrieval-augmented Q&A, slot-based onboarding flows, or multi-step assistants — all built from the same building blocks.
Why It Matters
For teams experimenting with prompts or running early-stage prototypes, this means you don't have to rebuild your system when it's time to go production.
- Prompts become safe, versioned infrastructure instead of brittle strings
- Models become swappable components with cost and reliability controls
- Workflows become maintainable graphs instead of tangled code
It's a way to take what works in prototyping — fast iteration, experimentation — and add the reliability, cost control, and observability that production demands.