Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation
Abstract
Building on the conceptual framework presented in our previous work on agentic AI for pharmaceutical research, this paper provides a comprehensive technical analysis of Tippy's multi-agent system implementation for drug discovery laboratory automation. We present a distributed microservices architecture featuring five specialized agents (Supervisor, Molecule, Lab, Analysis, and Report) that coordinate through OpenAI Agents SDK orchestration and access laboratory tools via the Model Context Protocol (MCP). The system architecture encompasses agent-specific tool integration, asynchronous communication patterns, and comprehensive configuration management through Git-based tracking. Our production deployment strategy utilizes Kubernetes container orchestration with Helm charts, Docker containerization, and CI/CD pipelines for automated testing and deployment. The implementation integrates vector databases for RAG functionality and employs an Envoy reverse proxy for secure external access. This work demonstrates how specialized AI agents can effectively coordinate complex laboratory workflows while maintaining security, scalability, reliability, and integration with existing laboratory infrastructure through standardized protocols.
Cite
@article{arxiv.2507.17852,
title = {Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation},
author = {Yao Fehlis and Charles Crain and Aidan Jensen and Michael Watson and James Juhasz and Paul Mandel and Betty Liu and Shawn Mahon and Daren Wilson and Nick Lynch-Jonely and Ben Leedom and David Fuller},
journal= {arXiv preprint arXiv:2507.17852},
year = {2025}
}