Data Source Automator
End-to-end pipeline: research → specifications → code generation with human review gates.
The Problem
Data engineering teams waste weeks researching data sources, writing specifications, and building extraction pipelines. The research phase alone can take days for complex APIs or scraping requirements.
The Solution
A multi-agent pipeline that automates the entire data sourcing workflow: research agents explore extraction methods, spec agents generate data models and requirements, and coding agents build microservices—all with human review gates.
Architecture
%%{init: {'theme': 'dark', 'themeVariables': { 'fontFamily': 'Inter', 'secondaryColor': '#1e293b', 'primaryColor': '#3b82f6', 'primaryBorderColor': '#60a5fa' }}}%%
graph TB
subgraph Research ["Research Phase"]
A["Supervisor Agent"] --> B["API Researcher"]
A --> B2["Download Analyst"]
A --> C["Scraping Analyst"]
B --> D["Method Selector"]
B2 --> D
C --> D
end
subgraph Spec ["Specification Phase"]
D --> E["Data Model Gen"]
E --> F["Requirements Writer"]
F --> G["Human Review"]
end
subgraph Impl ["Implementation Phase"]
G --> H["Tech Spec Agent"]
H --> I["Coding Agent"]
I --> J["Testing Agent"]
J --> K["Human Review"]
end
classDef default fill:#0f172a,stroke:#334155,color:#fff,stroke-width:1px;
classDef review fill:#3b0764,stroke:#a855f7,stroke-width:2px;
classDef agent fill:#0f172a,stroke:#3b82f6,color:#fff;
class G,K review;
class A,B,B2,C,H,I,J agent;
AI Agent
Process Step
Human Review
Tags
PythonMulti-AgentHITL
Outcomes
- 70% reduction in research-to-spec time
- Automated technical specification generation
- Human-in-the-loop quality gates at every phase