AI Engineer · advanced
Multi-Agent Autonomous Research System (Supervisor Pattern)
A system where AI agents (Researcher, Writer, Fact-Checker) collaborate under a Supervisor agent that manages workflow.
Project overview
This is the era of "Agentic AI." Companies want engineers who can build systems that DO things, not just say things. This project proves you can manage complex, multi-step autonomous logic.
Real-world use case
A market research team uses this to monitor competitors and generate weekly PDF summaries automatically.
Tools and technologies
- Python
- LangGraph
- Tavily
- Claude 3.5
- Redis
Skills you can demonstrate
Agentic Workflows, State Management, Tool Calling
Build roadmap
- Design the agent graph
Draw it in Mermaid first: Supervisor → (Researcher | Writer | Fact-Checker) → Supervisor. Decide on routing logic. - Define the shared state
Create a TypedDict with fields: task, research_notes, draft, fact_check_results, final_output. - Build each agent node
Each is a function that reads state, calls the LLM with its specific system prompt, and returns updated state. - Build the Supervisor node
This agent decides which node runs next based on the current state. Use structured output to constrain the decision. - Add error handling + retries
If a node fails, the Supervisor should retry or route to a fallback. Log everything to Redis for replay. - Add the human-in-the-loop checkpoint
Pause before the final output, request human approval via a CLI or web input, then finalize. This makes the demo.
Ways to extend the project
Integrate a "Human-in-the-loop" node where the AI pauses and asks for user approval before finalizing a report.
How to showcase your work
- GitHub: A Mermaid diagram in the README showing agent communication flow.
- LinkedIn: Video showing the agents collaborating live.
- Resume: "Architected a multi-agent system using LangGraph that reduced research time by 80%."