AI Harnesses for MLSs: Easier Workflows, More Oversight Suggested

A simple AI request can require several important checks

“Mark 123 Main Street pending.”

For an agent, the request is straightforward. For the systems supporting it, several questions need answers.

Which listing does the agent mean? Are they authorized to change it? What information is required for that status? Does the request need confirmation? Did the update actually succeed?

An effective AI experience needs to coordinate those steps.

At Solid Earth, that’s the direction of the AI experience we’re developing: conversational workflows that account for the permissions and requirements of MLS work.

What is an AI harness?

An AI harness is the software around a model that helps it perform work.

The model interprets a request and can propose the next action. The harness coordinates the process: supplying context, making tools available, managing multiple steps, and applying checks and approval requirements.

For MLS use cases, that process can include verifying identity, checking permissions, collecting required information, routing unusual activity for review, and recording results.

The interface is where the agent speaks or types. The harness coordinates what happens behind that interaction.

How does an AI harness differ from MCP?

Model Context Protocol, or MCP, provides a standard way for AI applications to connect with tools and data sources.

An MCP connection might expose a property-search tool or an authorized listing-update function. Its implementation can also include access controls, validation, and limits.

A harness coordinates how those tools are used within a larger workflow. It can work with MCP connections and other integrations.

For an MLS evaluating AI, both matter. The useful questions concern the controls and behavior of the complete system: what it can access, what it can do, and how each action is checked.

Put the required steps inside the task

Consider the pending-status request again.

A well-designed workflow would first identify the correct listing and verify that the requesting agent is permitted to change it.

Next, it would retrieve the requirements for that MLS. If a pending date or another required field were missing, it would ask for that information.

The application could then show the proposed change, request confirmation, submit through an authorized connection, and check the result.

A completed action would leave a record of the request and outcome.

This approach gives the agent a direct way to work while preserving important steps in the process.

Different AI prompts and actions need different controls

Searching for properties, sending information to a client, and changing an official record create different responsibilities.

A search may require restrictions on which records and fields are returned.

A client communication may require approved content, attribution, destination checks, or review.

An update may require specific authority, required fields, and explicit confirmation.

The controls should match the action. They also need to reflect the applicable MLS policies, data agreements, and legal requirements.

That’s why a useful implementation begins with clearly defined use cases.

Define where oversight applies

A credible AI system should explain the boundaries of its control.

An application can check the messages it sends, limit the exports it generates, and require approval for actions performed through its connected tools.

Information copied or changed outside that environment presents a different challenge. The same applies when data moves into another vendor’s product: the integration needs defined permissions and handling requirements.

A clear account of those boundaries helps an MLS decide which workflows to approve and which safeguards it needs.

Those demonstrations make the discussion concrete. They show how the experience behaves when the request is easy—and when it requires additional judgment or intervention.

Easier workflows. Oversight built in.

The opportunity for MLSs is to make everyday work more direct while keeping authority, requirements, and accountability visible throughout the process.

That’s the approach we’re developing at Solid Earth.

We’re inviting MLS leaders to explore the experience, bring their workflow questions, and discuss where conversational AI could support their members.