AI Agent for Laboratory Liquid Handling
QPillars (Internal R&D)
The Challenge
Liquid handling robots are essential in life sciences laboratories, but their control software has not kept pace with AI. Scientists spend hours manually programming pipetting sequences through rigid, vendor-specific interfaces - even for routine experiments. Non-technical lab staff cannot operate the instruments without extensive training. There was no standard way to connect AI agents to physical instruments with the safety guarantees that regulated environments demand: schema validation, human oversight, and full audit trails.
Our Solution
We built an agentic AI platform that replaces manual protocol programming with natural language control. Scientists describe experiments in plain English - the AI agent plans the workflow, validates every parameter against hardware constraints, presents the plan for human approval, and executes it on the instrument. The system uses a three-service architecture: a React frontend with real-time deck visualization, a FastAPI backend managing instrument state and labware tracking, and a CrewAI agent server connected via MCP (Model Context Protocol) - the open standard for AI-to-tool communication. A planner-executor pattern ensures the AI never sends unvalidated commands to hardware.
Results
- Protocol programming reduced from hours of manual configuration to seconds of natural language input
- Human-in-the-loop approval on every action - designed for GxP/FDA-regulated workflows
- MCP-based integration enabling any AI model to control instruments through a single open standard
- Modular architecture reusable across instrument types - plate readers, sample prep robots, chromatography systems
“We built LiquidBridge to prove that agentic AI can control lab instruments safely through an open standard. Every client conversation since has started with this demo.”
Iacob Marian
Technical Lead & Co-founder, QPillars
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