An AI agent can generate a valve command in milliseconds. That does not mean the intended fluid path exists in the instrument.
In an IVD analyzer or laboratory-automation system, a successful register write proves only that software sent a value. It does not prove that the valve was homed, that the requested port is physically connected to the intended reagent, that the previous liquid has been displaced, or that the pressure is safe for switching.
That distinction matters as teams begin using ChatGPT, Codex, Claude Code, Gemini, DeepSeek, and other AI tools to generate control code, test sequences, and troubleshooting workflows.
This article proposes a bounded control pattern for a future AI-agent layer. It uses the FOREACH MRV3 product page as a concrete engineering reference, but FOREACH does not currently claim that the MRV3 contains autonomous AI control. The AI workflow below is a design recommendation, not a description of a shipping AI feature.
1. Resolve route intent to a physical port map
An agent should never act on a vague instruction such as switch to wash.
It needs a versioned route map that resolves the semantic intent to a physical port, destination, tube, and allowed sequence:
{
"intent": "wash_to_waste",
"valve_id": "V1",
"requested_port": 12,
"source": "wash_buffer_A",
"destination": "waste_2",
"map_revision": "fluidics-2026-08-18"
}
If the route-map revision in the controller does not match the instrument configuration, the safe action is to stop—not to guess.
2. Verify home and current position
Power-up reset is useful, but a supervisory controller still needs evidence that the expected reference sequence completed and that the reported position is credible.
Before switching, check at least:
- the valve has completed its reset or homing sequence;
- the current position is known;
- the requested port is valid for the installed valve configuration;
- the last movement did not end in a timeout or position fault.
The FOREACH MRV3 page lists automatic reset on power-up. In an agent-managed system, that event should become an explicit verified state—not an assumption hidden inside startup code.
3. Check fluid and wetted-material compatibility
The agent needs more than a list of port names. It needs fluid metadata: chemistry, concentration, temperature, cleaning requirements, and compatibility with every wetted material in the route.
For example, the MRV3 page lists PCTFE, zirconia ceramic, and sapphire among the wetted materials. An engineering database can use that information as an input, but compatibility still has to be evaluated for the actual formulation and operating conditions.
If fluid identity is unknown or its compatibility record is missing, the route should be unavailable to autonomous execution.
4. Budget internal volume, carryover, and flush volume
A route can be mechanically correct and analytically wrong.
Internal volume changes the amount of liquid retained in a path and therefore affects flush demand, mixing, and carryover risk. The MRV3 reference page lists different channel diameters and corresponding internal volumes, including 15.8 uL, 10 uL, and 2.9 uL configurations.
The controller should combine valve volume with tube volume and downstream cell volume, then require a validated displacement factor:
required_flush = retained_route_volume x validated_displacement_factor
The factor is not universal. It should come from instrument testing with the real fluid pair, tubing, flow rate, and acceptance criterion.
5. Match pressure, bore, and interface constraints
The command is unsafe if the requested route violates the operating envelope.
The agent should compare:
- expected pressure against the permitted pressure range;
- selected channel bore against required flow and pressure-drop limits;
- connection type against the installed fittings;
- pump behavior against the transient created by switching.
The official MRV3 page lists a 0.7 MPa pressure rating, multiple channel diameters, and both 1/4-28UNF and 6-40UNF interfaces. Those are configuration inputs, not values an AI model should infer from a photo or product family name.
6. Enforce a no-flow switching sequence
An agent should not issue a port change while the pump is still pressurizing the line unless the complete fluidic design explicitly permits it.
A conservative sequence is:
- stop or decelerate the pump;
- confirm pressure is below the switching threshold;
- command the valve;
- wait for verified position or timeout;
- begin the validated flush sequence;
- restart process flow only after postconditions pass.
The MRV3 page lists switching within two seconds per revolution and under 100 milliseconds for adjacent ports. These times can inform a timeout budget, but they do not replace instrument-level validation.
7. Require feedback, fault handling, and a safe fallback
The agent must distinguish three states:
- requested: software asked for a position;
- reported: the controller returned a value;
- verified: the instrument satisfied the defined physical and analytical checks.
A compact command contract could look like this:
{
"intent": "wash_to_waste",
"requested_port": 12,
"preconditions": {
"pump_stopped": true,
"pressure_kPa": 0,
"route_map_current": true
},
"verified_state": {
"homed": true,
"reported_port": 12,
"fault": null
},
"postcondition": "flush_then_measure"
}
The fallback should be deterministic: stop the pump, preserve the fault context, prevent retries beyond a defined limit, and require operator recovery when physical state cannot be verified.
A small software guardrail
AI can propose an action, but a deterministic function should decide whether the action is eligible for execution:
from dataclasses import dataclass
@dataclass
class ValveContext:
homed: bool
pump_stopped: bool
pressure_kpa: float
route_map_current: bool
fluid_compatible: bool
requested_port: int
allowed_ports: set[int]
def may_switch(ctx: ValveContext, max_switch_pressure_kpa: float) -> bool:
return all([
ctx.homed,
ctx.pump_stopped,
ctx.pressure_kpa <= max_switch_pressure_kpa,
ctx.route_map_current,
ctx.fluid_compatible,
ctx.requested_port in ctx.allowed_ports,
])
The important architecture choice is separation of concerns:
- the AI layer interprets intent and proposes a plan;
- deterministic guardrails validate eligibility;
- the controller executes the command;
- sensors and state feedback verify the result;
- validated instrument logic decides whether processing can continue.
Product reality: what the reference valve actually provides
The FOREACH MRV3 ceramic multiport rotary valve is presented for fluid-path selection in automated analytical instruments. The official page lists 10-, 16-, and 24-channel options, several channel diameters and internal volumes, automatic power-up reset, optional drivers, and RS232/RS485 communication.
Those features make the product page a useful reference for discussing configuration-aware control. They do not, by themselves, turn a valve into an autonomous system. Safe operation still depends on the surrounding mechanics, electronics, firmware, tubing, fluids, sensors, test evidence, and application requirements.
The practical takeaway
The near-term value of an AI agent is not to bypass control engineering. It is to make the engineering state more explicit: route intent, configuration revision, compatibility evidence, carryover budget, interlocks, and recovery steps.
When those facts are machine-readable, AI can help generate test cases, review route plans, explain faults, and propose sequences. The final permission to move fluid should remain bounded by deterministic checks and verified physical state.
Disclosure: I am a member of the FOREACH team. The product facts in this article are paraphrased from the official MRV3 page linked above. The AI-agent architecture is an engineering proposal and is not a claim that the current product provides autonomous AI control.
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