Systems Design Case Study
AI Workflow Orchestration Platform
Designing a modular, observable system for orchestrating AI workflows, balancing flexibility, reliability, and cost control across models and execution contexts.
The Problem
Teams wanted to build AI-powered features: document generation, summarization, classification, automation—but ad-hoc scripts and single-call APIs quickly broke down as complexity increased.
Hardcoded prompts, limited observability, and tight coupling between UI and model logic made systems brittle, expensive, and difficult to evolve.
Constraints
- • Multiple model backends (local and hosted)
- • Long-running or multi-step workflows
- • Cost awareness per execution
- • Safe handling of user inputs and outputs
- • Observability without leaking sensitive data
- • Prompt iteration without redeploying applications
Architecture Concept
AI interactions are treated as workflows, not direct model calls. UI never talks directly to models—it talks to workflows.
Key Decisions
Workflow graphs over single prompts
Graph-based workflows allow steps to be modified, reordered, or replaced without rewriting consuming applications.
Model-agnostic orchestration
Workflows define capabilities, not vendors, allowing models to be swapped based on cost, availability, or performance.
Async execution by default
AI calls are unpredictable in duration. Background execution enables retries, recovery, and non-blocking UX.
Prompt iteration as configuration
Prompts and parsing logic live in workflow definitions, not application code—allowing rapid iteration without redeploys.
Explicit inputs and outputs
Strict schemas prevent silent failures and hallucinated structure from leaking into downstream systems.
Tradeoffs Considered
| Option | Outcome |
|---|---|
| Direct LLM calls from UI | Rejected (tight coupling, poor observability) |
| One workflow per feature | Rejected (duplication and drift) |
| Vendor-locked SDKs | Rejected (cost and portability risk) |
Outcome
- • Reusable AI workflows across multiple features
- • Faster iteration on prompts and logic
- • Predictable cost behavior
- • Clean separation between UI and AI logic
The system treats AI as infrastructure, not magic.