Dan

Case Study + Deep Dive: Telemedicine Support Agents with LangGraph/MCP - Dan Mason

Jun 22, 2025 · 1:56:13

Dan Mason of Stride presents a case study on building autonomous agents for telemedicine support, replacing a human-driven button-pushing workflow with LangGraph, Claude, MCP, and a Node.js/React/MongoDB stack, achieving roughly 10X capacity increase. The LLM-driven virtual operations associate ("Ava") assesses patient messages, updates state with anchors and scheduled messages, and passes proposals to an evaluator agent that scores confidence and complexity, escalating to humans below 75% confidence. Mason explains how treatment "blueprints" in Google Docs are read directly by the LLM instead of using RAG, enabling new treatments to be added without writing code. He details the eval system using LLM-as-a-judge via PromptFu and retry logic for tool-calling errors, and discusses trade-offs around confidence scoring, prompt caching, and model selection (Claude for steerability). The team includes two software engineers, one designer, and Mason, who wrote most LangGraph code with AI assistance (Kline).

Building with Anthropic Claude: Prompt Workshop with Zack Witten

Aug 17, 2024 · 1:34:56

In this live prompt engineering workshop, Anthropic's Zack Witten and Jamie Neuwirth demonstrate techniques for improving Claude prompts in real time, using the Anthropic console. They show that XML tags clearly separate prompt sections, that placing instructions after information improves adherence, and that prefilling assistant responses with opening JSON or tags reliably forces JSON output without preamble. Witten advises using stop sequences and code to handle formatting rather than over-prompting, and emphasizes that few-shot examples—especially contrastive pairs with reasoning—drive more improvement than any other technique. The workshop covers role-playing multiple personas by routing via code, mitigating hallucinations by extracting quotes before summarization, and grading translations with chain-of-thought and fine-grained examples.