AI Research Orchestrator / Multi-Agent Research Scientist
Developed an autonomous multi-agent research system that plans literature reviews, retrieves academic papers, synthesizes findings, and generates structured research reports.
Related Projects
SYSTEM ARCHITECTURE
Offline-demo mode uses deterministic agents and simulated workers where external services are unavailable.
Problem
Research workflows are fragmented across search, ranking, reading, and synthesis. The goal was to coordinate those steps into a repeatable agent-driven system.
Architecture
Planner, analyst, architect, critic, and consensus stages coordinate paper retrieval, citation analysis, literature summaries, and structured report generation.
Challenges
- API rate limiting
- Paper ranking
- Agent orchestration
- Tool abstraction
- Offline execution mode
Benchmarks
Built research planning, paper retrieval, citation analysis, structured reports, and literature summary flows using external academic data sources and LLM-driven orchestration.
Lessons Learned
- Specialized agents make research workflows easier to reason about
- Consensus steps are useful when synthesis needs to be grounded in sources
- Tool boundaries matter when mixing retrieval, ranking, and writing