video1.0<iframe src="https://www.loom.com/embed/2bc96c7d096e49ecb84f2702f9ccba94" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/2bc96c7d096e49ecb84f2702f9ccba94-14a56d19d005a37e.gif221.929Autonomous Agentic Research Framework: High-Fidelity Synthesis & Factual Grounding - 23 January 2025Developed a sophisticated Planner-Synthesizer Agentic Workflow in Langflow designed for high-fidelity data retrieval.
Logic: Implemented a dual-node LLM architecture where the primary model acts as a "Strategic Planner," decomposing user prompts into optimized search queries.
Fact-Grounding: Utilized Tavily AI for real-time RAG (Retrieval-Augmented Generation), forcing the agent to ground all claims in live web data and academic sources.
Output: Engineered a final synthesis layer that transforms raw search data into professional reports with clickable citations, effectively eliminating LLM hallucinations.