Why the Same AI Model Works Better in Some Tools

You can use the same model in two AI tools and get surprisingly different results.

One keeps track of what you’re doing, finds the right information, uses tools, and keeps working. Another loses context, gets stuck, or gives you a weak answer and stops.

The difference is often the software around the model.

That software is called a harness.

In this live session, I’ll break down the difference between a model, a harness, and an agent, then show you the five jobs a harness needs to handle well.

More importantly, we’ll use that mental model to diagnose what actually went wrong when AI disappoints you.

Did the model lack context?
Could it access the right tools?
Did it forget something important?
Was the workflow poorly orchestrated?
Was the system preventing it from doing what the task required?

Once you can see those pieces, “the AI gave me a bad answer” becomes a much more useful question:

What needs to change?

This is for you if…

• You use multiple AI tools and wonder why the same model performs differently across them.
• You’ve seen an agent lose context, get stuck, or produce weak work and couldn’t tell why.
• Your first instinct when AI fails is to rewrite the prompt or switch models.
• You choose or set up AI tools and want to understand what actually makes one better for a particular job.

What you’ll get…

• A clear mental model for models, harnesses, and agents.
• The five jobs a harness needs to handle.
• A practical way to diagnose why AI work breaks down.
• A better framework for choosing and configuring AI tools.

See you there!