---
id: intro.correctness-infrastructure
title: What is correctness infrastructure?
summary: Learn how five connected capabilities turn specialist judgment into cases, executable standards, evaluation evidence, and improvement.
kind: concept
product_area: introduction
status: stable
updated: 2026-08-22
canonical: /docs/introduction/correctness-infrastructure
---

# What is correctness infrastructure?

Correctness infrastructure is the operating layer that lets a team specify, test, and improve the behavior of specialist AI. It connects the behavior space that matters, the expert judgment that defines acceptable behavior, the cases that challenge a system, the evidence produced by repeatable evaluation, and the engineering work that follows.

{% visual-hero src="/docs-assets/assets/correctness-infrastructure-workbench.png" alt="Workbench connecting coverage design, expert judgment, cases, evaluation evidence, and improvement." %}
The visual is a category anchor. The selectable capability names and current product mappings below are authoritative.
{% /visual-hero %}

## Definition

Teammately expresses correctness infrastructure through five capabilities:

1. **Coverage Engineering** designs what the benchmark must represent.
2. **Correctness Elicitation** captures policies, exceptions, applicability, and expert rubrics.
3. **Weave** constructs cases, variants, multimodal materials, and supported worlds from the coverage design.
4. **Trialground** runs harnesses and weights against benchmark cases and records rubric evidence.
5. **Coevolve** explores stronger candidate branches and missing correctness or coverage.

The sequence is connected rather than strictly linear. An evaluation failure may expose a missing rubric. An expert contribution may reveal a new coverage facet. An Improvement Session may find that the benchmark needs a harder case before a harness change can be trusted.

## Decision checkpoint

| Question | Inspect first | Expected durable result |
| --- | --- | --- |
| Does the benchmark represent the important behavior space? | Coverage Engineering | Dimensions, Project Topics, Case Construction Patterns, and benchmark coverage state |
| What should count as correct for this situation? | Correctness Elicitation | Governed policies, applicability, and binary rubrics |
| Do we have cases that exercise the difficult combinations? | Weave | Reviewable cases, variants, materials, and dataset membership candidates |
| How did this candidate behave against the benchmark? | Trialground | Runs, responses, Rubric results, comparisons, and rankings |
| What change is justified by the evidence? | Coevolve | A bounded Improvement Session with candidates and evaluation receipts |

> Keep the category centered
>
> Evaluation execution is one capability inside correctness infrastructure. The category also includes the expert-derived specification, deliberate coverage, case construction, and evidence-backed improvement that make evaluation meaningful.

## From expert effort to reusable infrastructure

Expert time is most valuable when it resolves ambiguity that agents and engineers cannot settle from existing evidence. Teammately therefore prepares a structured contribution: the relevant cases, reference materials, candidate interpretations, possible policies, rubric questions, and unresolved conflicts. Once an expert responds, the contribution can affect more than the immediate task. It can refine the coverage map, materialize a policy or rubric, qualify a case, or identify the next evaluation.

This creates a higher return on expert effort. The product does not ask specialists to repeatedly label disconnected outputs; it preserves why a judgment was made and where that judgment applies.

## Product realization

The project foundation holds reusable context and assets. Correctness Governance owns policies and rubrics. Coverage Facets owns Dimensions, Project Topics, and Case Construction Patterns. Assets owns cases, harnesses, and other project-level resources, including reusable Comparison Directions and Review Screens. Agent Setup gives agents reusable project understanding through Project Context and Reference Materials. Expert Contributions configure scoped agent behavior and select the presentation and variation assets needed for the work.

A benchmark workspace then creates a scoped operational loop. Benchmark Datasets defines the case set and snapshots. Coverage Management exposes represented and missing behavior. Expert Contributions requests governed human work. Benchmark Evaluations produces versioned evidence. Improve starts from that evidence and coordinates candidate exploration.

{% layer-map title="Five capabilities of correctness infrastructure" %}
{% /layer-map %}

{% example-demo title="Policy exception handling" %}
A support assistant must answer exception requests. Coverage Engineering identifies exception type, customer state, and source freshness as important facets. Correctness Elicitation records the expert rule for when an exception can be stated or must be escalated. Weave prepares ordinary, conflicting-source, and boundary cases. Trialground shows that a candidate handles ordinary cases but invents approval on ambiguous ones. Coevolve tests a harness change and keeps the resulting candidate tied to the exact failed cases and rubric evidence.
{% /example-demo %}

## Related workflows

{% related-card-grid title="Related workflows" %}
- [The correctness lifecycle](/docs/introduction/correctness-lifecycle)
- [The correctness loop](/docs/product-loop)
- [Product quickstart](/docs/quickstart)
{% /related-card-grid %}

## Related reference pages

{% related-card-grid title="Related reference pages" %}
- [Product map](/docs/getting-oriented/product-map)
- [Key objects and relationships](/docs/getting-oriented/key-objects-and-relationships)
- [Object model](/docs/object-model)
{% /related-card-grid %}

## Source confidence

Doctrine-backed: this page defines the current public category and capability model. Product-surface pages provide code-backed operational detail.