Standard laboratory automation executes predefined, linear tasks reliably across high-volume runs. Yet these setups remain strictly reactive, halting the workflow for human approval when a protocol needs pivoting or a decision confirmed.
This whitepaper describes how a hierarchical agent architecture takes over those contextual decisions so protocols adapt and continue automatically. By separating reasoning from execution, the platform lets multi-agent networks replan mid-workflow while capturing the underlying logic for regulatory review.
Download this whitepaper to explore
- How Model Context Protocol (MCP) lets agents orchestrate samples between instruments
- Reinforcement learning that tunes protocols against live run data
- How continuous verification meets 21 CFR Part 11 without a signature
