Inspiration

Traditional background checks are manual and error-prone. We wanted to create an AI agentic system that automatically coordinates multiple specialized agents to gather and analyze information intelligently.

What it does

Backgrounder is an AI agentic system using MCP (Model Context Protocol) agents. An intelligent orchestrator coordinates specialized scraper and analyzer agents to perform comprehensive background verification, delivering structured reports with full observability.

How we built it

AI Agentic Architecture: MCP-based orchestrator that plans and coordinates agent workflows
LlamaIndex Integration: Advanced document analysis and knowledge retrieval
Datadog Observability: Complete tracing, monitoring, and logging across all agents
Bright Data: High-performance web scraping and data extraction
Next.js UI: Modern interface for user interaction and result visualization

Challenges we ran into

Coordinating multiple AI agents with different capabilities and response formats
Implementing reliable MCP protocol communication between agents
Ensuring consistent data flow between scraping and analysis agents

Accomplishments that we're proud of

Built a fully functional AI agentic system with intelligent orchestration
Successfully integrated LlamaIndex and Bright Data for comprehensive search and analysis
Implemented comprehensive Datadog observability across all agents
Created MCP-based architecture for agent coordination

What we learned

LlamaIndex provides powerful document analysis capabilities for background verification
Datadog observability is crucial for debugging distributed AI agent systems
Bright Data offers reliable web scraping for comprehensive data gathering
Agentic AI systems require careful orchestration and error handling