The Model is not the Product Anymore

When I was a kid, we always fought over who had the better CPU or faster RAM. We knew the specifications of every computer in our friends’ houses. Think of models as modern CPUs and the harness as a new operating system.

A year ago, most AI conversations started with one question: Which model is better? Today, who cares which tests a model passes? When was the last time you analyzed benchmarks and model rankings?

Today, we are talking about harnesses. We are talking about what they can do and how much effort they require from us.

A harness manages:

• System Prompt
• Tool Descriptions
• Skill Index
• Memory
• User and Tool History
• User Prompt

It controls:

• Model
• Agent loop
• Skills
• Tools
• Context Management
• Session
• Permissions
• Subagents
• Gateway
• Computer / Browser / Terminal
• User Interface

How Do You Actually Compare Harnesses? #

Which harness lets me delegate the largest useful unit of work with acceptable supervision?

What practical capabilities does a harness add to an LLM? #

Models provide intelligence. Harnesses turn intelligence into labor. Intelligence is something you ask — labor is something you delegate. The practical shift is from asking for another answer to handing over a useful unit of work and judging the result.

That changes the unit of value. A clever answer makes a good demo, but completed work can be measured in time, capacity, cost, or risk.

The model sets the ceiling. The harness determines how much of that intelligence survives contact with real work.

Where Do Today’s Harnesses Still Suck? #

• Context is still fragile. Agents forget why decisions were made.
• Long-running work is unreliable. Twenty successful tool calls don’t mean the twenty-first won’t destroy the plan.
• Multi-agent systems sound better than they often work. More agents can mean more coordination overhead rather than more output.
• Memory isn’t solved. Saving everything isn’t memory. Good memory requires deciding what is worth remembering.
• Permissions are crude. Today’s choice is often somewhere between “Ask me constantly” mode and “You Only Live Once” mode.
• Observability is poor. When an agent spends 40 minutes going in the wrong direction, understanding why can still be painful.

Open Harness or Integrated Harness? #

One view is that the best agent experience comes from tightly integrating the model, harness, tools, cloud environment, desktop application, identity, and memory.

The other view is that the harness should be independent from the model. Your tools, skills, memory, workflows, and company context should belong to you, not to a model provider.

The harness becomes the stable layer, while models such as Claude, GPT, Gemini, or open-source models become replaceable components.

What is the future? #

Today, most AI products still follow a simple model: a user interacts with an AI application, and that application talks to a model.

A year from now, the harness may become the main layer between the user and the models. The desktop apps will become operating systems for work.

A personal or company harness could manage multiple specialized agents. One agent might handle coding, another might handle browser work, another might handle business processes, and others could handle research, operations, or support.

The harness could decide which model to use for each task and route work between GPT, Claude, Gemini, open-source models, or whatever comes next.

Models would increasingly become replaceable compute underneath that layer. We will definitely see providers protect their integrated harnesses.

Andrew Korolov is a AI Solutions Architect and founder of MavenSolutions.