Agentic AI for Laboratory Automation

Agentic AI for lab automation means AI agents that can plan, validate, and execute multi-step laboratory workflows on top of your existing instruments and scheduler. Unlike static automation (scheduled scripts, fixed protocols), an agent can adapt to new requests, ask for human approval, react to instrument errors, and replan when the unexpected happens. The 'agentic' part is the reasoning loop; the 'lab automation' part is the workflow domain.

Off-the-shelf orchestrators are platforms - you adopt their scheduler, their data model, their cloud, often their preferred instrument vendors. We build agents that sit on top of YOUR existing automation. Your scheduler keeps running. Your instrument fleet stays as-is. We add the agentic layer that works across what you already have. Source code yours from day one, kickoff in days, fixed price per engagement.

We start with a 30-minute technical call to scope ONE specific workflow end-to-end. Then a fixed-price productized engagement (8 weeks) builds the agent: planner role, simulator, approval gate, executor, run history, audit trail. Source code is yours. Two weeks of bug-fix support are included. After that, we either hand off completely or extend month-by-month for follow-on workflows. Request a one-pager for full pricing.

LLM-agnostic by design. The agent works with Claude, GPT, your own self-hosted model, or whatever you choose. Frameworks: LangGraph for production reasoning loops, CrewAI for simpler workflows, OpenAI Agents SDK or PydanticAI when those fit better. We pick the framework that matches your team's stack - never lock you to one. Underneath, every workflow runs through MCP tools so the framework can be swapped without rebuilding.

Three layers: (1) Pre-tool-call validation - the MCP server re-fetches state and re-checks preconditions before every call. (2) Plan simulation - the entire plan is walked against a digital-twin state model before any physical motion. (3) Human approval gate - the operator reviews the plan and explicitly approves before execution. We have shipped this pattern in our LiquidBridge demo and it is what we deliver as default.

Async events from the instrument flow back to the agent through MCP. When an error fires (clog, timeout, hardware fault), the agent surfaces it to the operator with the suggested action and either retries (idempotent operations only), aborts, or replans. Destructive operations stay manual by policy - the operator approves each one. The operator stays in control.

It is the Custom AI Agent bundle, our Showable productized engagement. The MCP layer underneath is the AI-Ready Instrument bundle - sometimes ordered together, sometimes the customer already has MCP servers in place. For ongoing co-development, we extend month-by-month under the Component Ownership bundle. See [/agentic-ai](/agentic-ai) for the umbrella picture.