AI operator interventions.
An intervention is a targeted change to an operator's workflow, tooling, training, or operating conditions, measured with pre/post comparison against a declared target metric. MO§ES™ has tested 12 interventions in the synthetic demo cohort. Every outcome join is labeled ASSOCIATION — never CAUSATION.
What an intervention is.
An intervention is a deliberate change to an operator's environment, workflow, or tooling, designed to improve a specific target metric. Every intervention declares its target metric and follow-up window before it begins. Results are measured, not assumed.
The process is simple: measure the operator's baseline, apply the intervention, re-evaluate after the follow-up window, compare target and non-target metric deltas, and retain or discard the intervention based on the evidence. The follow-up window in the synthetic demo is a 30-day period, consistent with the cohort's observation window.
What has been tested.
In the synthetic demo cohort of 50 operators across 1,668 observations, 12 interventions were tested against declared target metrics. Each intervention was designed to address a specific pattern identified through diagnostic analysis.
Target: Leverage. Train operators on context window management and reuse patterns.
Target: Yield. Provide structured prompt templates to reduce input overhead.
Target: Token SNR. Switch operators to a model with better output efficiency.
Target: Construction. Grant access to 21 MCP tools (16 read + 5 write) for structured context building.
Target: Composite Score. Redesign the operator's task workflow to reduce input redundancy.
Target: Leverage. Enable session persistence to encourage context reuse across turns.
Target: Yield. Add an output review step to reduce wasted generation.
Target: Composite Score. Enable access to multiple AI providers for task-appropriate model selection.
Target: Token SNR. Remove stale context before sessions to improve signal quality.
Target: Construction. Coach operators on building reusable structured context.
Target: Divergence. Targeted support for high-usage/low-performance operators.
Target: Composite Score. Align operator workflows with one of 15 eval families for consistent measurement.
How outcomes are evaluated.
Every intervention declares a target metric and a follow-up window before it begins. After the window closes, the system compares pre- and post-intervention values on both the target metric and non-target metrics.
The change in the metric the intervention was designed to improve. A positive delta means the intervention produced a measurable change in the intended direction.
Changes in metrics the intervention was not designed to affect. Large non-target deltas may indicate side effects — positive or negative — that warrant further investigation.
The system reports both deltas. An intervention that improves the target metric while degrading non-target metrics is not automatically retained. An intervention that does not produce a measurable change in the target metric is not automatically discarded — the system says so, and the intervention may be retested with adjustments.
ASSOCIATION — never CAUSATION.
A correlation between an intervention and a business metric is not proof that the intervention caused the business change. Outcome joins are labeled ASSOCIATION — never CAUSATION.
- ASSOCIATION, not CAUSATION. Internal metric deltas and external outcome deltas are kept in separate fields. Join results carry ASSOCIATION labels and governance metadata. A correlation is not proof of causation.
- Not an employee ranking tool. DEVELOPMENTAL labels mean results route workflows, not people. No adverse employment actions are permitted from pilot data.
- Target metric is declared in advance. The system does not retroactively select metrics to make an intervention look successful. The target is declared before the intervention begins.
- Falsifiability is built in. If an intervention does not produce a measurable change in the target metric, the system says so. If a diagnosis cannot be distinguished from an alternative, the system says so.
- Validation required. Outcome joins between interventions and business metrics require separate validation through experimental design. Without that validation, joins remain ASSOCIATION.
Every intervention record carries provenance: target metric, follow-up window, pre/post deltas, evidence label (ASSOCIATION), decision-use label (DEVELOPMENTAL), and synthetic-data flag where applicable.
Read alongside.
Identifies operators who may benefit from targeted intervention.
Benchmark class 6 — pre/post intervention comparison.
The target metric for several interventions.