Case Study · AI/ML Engineering / LangChain Development
Multi-Agent AI Research System - Autonomous Research & Report Generator
Built an autonomous multi-agent AI research system that automates the entire research workflow — from web searching and in-depth page reading to draft report generation and self-critique. Given any topic, the system deploys multiple AI agents that collaborate: one agent searches and scrapes relevant sources, another synthesizes findings into a structured report, and a critic agent reviews and refines the output for accuracy and completeness. The system provides full transparency with dedicated views for the final report, critic feedback, search results, and raw scraped content, making the entire agentic pipeline auditable.
The challenge
Manual research workflows are slow and shallow — searching, reading sources, drafting, and fact-checking can take days per topic. The goal was to automate the entire pipeline into one autonomous system.
Our solution
A LangChain multi-agent research system: a team of agents that web-search, deep-read pages, draft reports, and critique their own output before it's final. Results are surfaced in a Streamlit UI with tabbed views for the report, critic feedback, search results, and raw content — every claim sourced.
Development process
How we shipped it
Agent architecture design
Search & retrieval pipeline
Report drafting & self-critique loop
Source citation & validation
UI for transparency
Testing on live topics
Technology
The stack we used
Outcomes
Key results
Automated end-to-end research pipeline reducing manual research time significantly
Self-critique loop improves report accuracy through agent-based review before final output
Structured, source-cited reports with 5+ reputable references pulled automatically per topic
Full transparency with tabbed views for report, critic feedback, search results, and raw content
Successfully tested on medical/health research topics with accurate, well-sourced output
More work
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