用五个专业智能体自动完成药物研发实验流程
Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation
- 五类专用智能体通过标准协议协同工作
- 系统支持实验室全流程自动化，可稳定部署
- 适合医药研发团队与AI工程化落地场景
在前期提出智能体驱动药物研发概念框架的基础上，本文详述了用于药物发现实验室自动化的Tippy多智能体系统技术实现。系统采用分布式微服务架构，包含监督、分子、实验、分析和报告五个专用智能体，通过OpenAI Agents SDK进行编排，并借助模型上下文协议（MCP）访问实验室工具。架构涵盖智能体专属工具集成、异步通信模式及基于Git的配置管理。生产部署采用Kubernetes容器编排、Helm图表、Docker容器化及CI/CD流水线实现自动化测试与部署。系统集成向量数据库支持RAG功能，使用Envoy反向代理保障外部安全访问。本工作证明，专业化智能体可通过标准化协议有效协调复杂实验流程，同时确保安全性、可扩展性、可靠性及对现有实验室基础设施的兼容性。
原文摘要 · Abstract (English)
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.
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