Leverage recent agent framework enhancements such as modular orchestration, plug-and-play components, and concurrency controls to streamline AI development workflows.
Evaluate when to fine-tune open-source models versus adopting agent-based frameworks by mapping project requirements to system capabilities before implementation.
Integrate the A2A protocol with existing Multi-Chain Proxy (MCP) systems to build advanced AI agents, leveraging the natural synergy between protocols.
Integrate LLM logic into an agentic engine using existing AI developer tools to observe, iterate, and refine complex AI behaviors without building custom infra from scratch.
Use LangChain and LangGraph with Claude Code to ingest free datasets and spin up an agentic AI solution in a couple of hours for quick experimental validation.
Proposes prioritizing enhancements to internal reasoning chains (personas, debate, improved context) instead of building custom fine-tuned models for specialized domains.
Generate default diagnostic questions (e.g., smoking, alcohol use, occupation) from a few-shot example prompt to drive the first round of patient queries.
Structure diagnostic reasoning into discrete debate rounds with metrics for reasoning quality, confidence, and cumulative cost to decide when to proceed to diagnosis.
Use multiple AI agent perspectives engaging in a debate to drive high-performance medical reasoning without ordering diagnostic tests by comparing divergent viewpoints.
Implement case ID tracking with an ‘ignore if same case’ rule to manage multi-turn or returning-user sessions, preventing duplicate initialization of medical cases.
Design a medical AI agent as a graph of subagents—initial consideration, primary/secondary considerations, biases check, contradictory evidence, and final assessment—connected to diagnostic tools and patient interaction modules.
