AI Engineer · intermediate
Multi-Agent Research & Execution System
A collaborative AI system where specialized agents (Researcher, Writer, Fact-Checker) work together to autonomously generate reports.
Project overview
Demonstrates the ability to build autonomous systems that go beyond simple chat, showing you can handle complex workflows and tool integration — a top requirement for AI Engineers today.
Real-world use case
Financial services teams use this for automated market research and competitor analysis reports that used to take analysts a week to produce.
Tools and technologies
- CrewAI
- LangGraph
- Python
- OpenAI/Claude API
Skills you can demonstrate
Agent Orchestration, Tool-calling, State Management
Build roadmap
- Pick your framework
CrewAI is beginner-friendly; LangGraph gives you more control. Start with CrewAI for your first build. - Define the agents
Write clear system prompts for each agent: Researcher (searches + summarizes), Writer (drafts), Fact-Checker (verifies). Give each distinct tools. - Give them tools
Hook up web search (Tavily or Serper), a calculator, and a file-writer. Each tool should have a clean JSON schema. - Orchestrate the workflow
Define the task sequence: Research → Draft → Fact-check → Revise. Let the framework handle the handoffs. - Add logging + observability
Use LangSmith or simple JSON logs to track every agent decision. This is what makes the demo impressive. - Generate a real report
Pick a research topic (e.g., "EV market in Miami"). Run the full flow and publish the output. This IS your portfolio piece.
Ways to extend the project
Use AI to automatically generate the agent personas and task descriptions based on a high-level goal from the user.
How to showcase your work
- GitHub: Architecture diagram + full agent logs showing decision-making.
- LinkedIn: Video demo of agents collaborating in real-time.
- Resume: Focus on "Autonomous Agentic Workflows".