Adaptive Lab Operations

Problem

Lab teams often lose time coordinating manual runbooks across instruments, personnel, and shared workstations. The core issues are scheduling conflicts, version drift in protocols, and unclear error recovery.

Context and constraints

• Heterogeneous instrument interfaces
• Frequent changes in assay parameters
• Limited tolerance for silent failures
• Need for traceability without heavy enterprise tooling

Approach

The project uses a small control layer that:

• normalizes protocol inputs,
• validates run constraints before execution,
• streams status and error context into an auditable log,
• exposes a minimal dashboard for human oversight.

For this MVP, everything is modeled around deterministic steps and clear handoff boundaries between human operator and automation system.

Architecture

1. Intent layer: receives scheduling requests and converts domain constraints into a normalized task graph.
2. Execution layer: executes verified steps against local robot adapters.
3. Observation layer: captures state, error context, and completion metrics for replay.

Results

In simulation, this baseline architecture improved completion consistency by reducing edge-case misses between manual handoffs. Production use is still in setup.