AI :: Agent :: About :: AI Agents in Action: How Research Agents Solve Complex Problems
⪼ Made with 💜 by Polyglot.
This video is a tutorial aimed at educating viewers about the structure and best practices of multi-agentic AI systems designed for research tasks. The intent is to educate and inspire data scientists, developers, and knowledge workers to leverage agent frameworks like LangGraph, Crew AI, and LangFlow to automate and enhance complex research workflows. The video breaks down the research process into five key steps, explaining how different specialized agents can be orchestrated to define objectives, plan, gather data, refine insights, and generate structured answers—emphasizing trust, safety, and quality in output.
- Define the research objective with a clear, focused goal
- Plan the research approach, breaking down questions and identifying sources
- Gather data from prioritized, reliable sources using retrieval agents
- Refine insights with analysis, validation, and recursive planning
- Generate a structured, human-readable answer with writing agents
- Assign specific roles to agents: strategist, data miner, analyst, writer
- Use open-source frameworks (e.g., LangGraph, Crew AI, LangFlow) for modularity
- Prioritize trustworthy knowledge sources to avoid misinformation
- Implement validation layers to ensure credibility and filter out bias
- Emphasize safety by guarding against data poisoning and hallucinations
- Benchmark and test agent outputs for quality and alignment with objectives
- Focus on both productivity (speed) and fruitfulness (quality) in research output
- Responsible design ensures research benefits the common good, not just efficiency