Start Building an AI Harness Effectively

Stop Renting Intelligence. Start Building AI Harnesses.

Every new frontier AI model creates the same conversation.

"Should I pay for it?"

I think that's the wrong question.

The better question is:

"What can I build with it today that will continue creating value long after I stop using it?"

That shift in mindset is the difference between using AI as a tool and harnessing AI as infrastructure.

A recent post on X proposed an interesting experiment: before Claude Fable 5 moves to usage-based pricing, use it to extract its reasoning style into a reusable operating manual. Then use that manual to guide a less expensive model such as Opus 4.8 (or any other capable model) for day-to-day work. The underlying idea is simple: own the workflow instead of depending entirely on the premium model.

Whether this specific technique delivers the expected results remains to be validated. But the bigger lesson is much more important.

It introduces a concept I've been thinking about for quite some time:

Harnessing AI instead of merely consuming AI.

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The model isn't the asset

Models will improve.

Models will become cheaper.

Models will change pricing.

Models will disappear.

We've already watched this happen repeatedly.

A model that seems revolutionary today may become a commodity six months from now. Anthropic's move toward usage-based pricing for Claude Fable 5 is simply another reminder that access models change over time.

If your competitive advantage depends entirely on having access to one specific model...

...your advantage isn't really yours.

But if you use that model to create something reusable...

...that asset remains yours.

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Think like an engineer

Software engineers learned this lesson decades ago.

Nobody rewrites authentication for every application.

Nobody writes a database engine every weekend.

Nobody rebuilds Kubernetes before deploying an application.

We build reusable components.

Frameworks.

Libraries.

Patterns.

Infrastructure.

Those components become leverage.

AI should be no different.

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What is an AI harness?

I've written before that AI Harnesses exist to reduce friction, amplify capability, and deliver outcomes.

This idea fits perfectly here.

A harness isn't the model.

A harness is everything surrounding the model that makes it consistently useful.

That includes:

• Prompt libraries
• Operating manuals
• Decision frameworks
• Evaluation rubrics
• Checklists
• Workflow templates
• Knowledge bases
• Verification procedures
• Memory structures
• Context engineering
• Automation workflows

These are reusable assets.

The model simply powers them.

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Rent intelligence. Own systems.

Imagine hiring the world's best consultant.

Would you ask them to answer your emails every day?

Probably not.

You would ask them to help design:

• your operating procedures
• your sales playbook
• your hiring framework
• your onboarding process
• your pricing strategy

Once those exist, your team can execute them repeatedly.

The consultant becomes an accelerator.

Not a permanent dependency.

Frontier AI models should be treated exactly the same way.

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The 80/20 strategy

This is in fact the strategy that I am using with Clawne Me and My Clawster in my own AI workflows. Cost efficiency is key: I reserve the premium models for tasks that create leverage and use more economical models for routine execution.

Suppose a premium model costs significantly more than your daily model.

Instead of running every task through the expensive model:

Use it for the work that creates leverage.

Examples:

• designing prompt architectures
• creating evaluation frameworks
• documenting reasoning processes
• generating reusable workflows
• defining quality standards
• building knowledge maps
• creating playbooks

Then use more economical models for:

• summarization
• customer responses
• documentation updates
• report generation
• email drafting
• content adaptation
• routine coding
• repetitive analysis

The expensive model becomes your architect.

The affordable model becomes your operator.

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This requires discipline

Here's the difficult part.

Most people won't do it.

Not because it's technically hard.

Because it requires changing how they think.

It's much easier to open ChatGPT or Claude every morning and ask:

"Help me with today's work."

Instead, you need to ask:

"How can I make sure I never have to solve this problem again?"

Those are completely different questions.

One produces output.

The other produces assets.

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Planning before prompting

This is where planning becomes critical.

Before opening any AI model, classify the task.

Ask yourself:

Is this execution?

Or...

Is this infrastructure?

Execution tasks disappear after they're completed.

Infrastructure tasks continue producing value.

For example:

Execution

• Write today's email
• Summarize today's meeting
• Review one document

Done.

Finished.

Value expires quickly.

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Infrastructure

• Create an email framework
• Design a meeting summary template
• Build a document review methodology

These continue paying dividends every time you reuse them.

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Your library becomes your competitive advantage

Imagine doing this consistently for a year.

Instead of thousands of isolated conversations...

You build:

• 40 reusable workflows
• 60 prompt templates
• 20 evaluation frameworks
• 15 decision trees
• 10 operating manuals
• dozens of specialized checklists

Suddenly your AI system becomes smarter.

Not because the model improved.

Because your harness improved.

That's leverage.

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Don't blindly trust the experiment

Now, an important disclaimer.

The X post suggests extracting Fable's reasoning style into an operating manual and reproducing it with another model.

Will it work perfectly?

Probably not.

Reasoning quality depends on more than prompts alone. It also depends on model architecture, training, inference capabilities, and other characteristics that cannot simply be copied.

However...

That's not the point.

The experiment itself is valuable.

If it improves your results by 20%, 30%, or even 50%, you've learned something useful.

Run the experiment.

Measure it.

Compare outputs.

Iterate.

Treat AI like engineering.

Not magic.

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Harnesses compound over time

One of the biggest misconceptions about AI is believing productivity comes from better prompts.

I don't think that's true.

Productivity comes from better systems.

Every reusable asset you create becomes another component inside your AI harness.

Eventually your process looks something like this:

Problem
      ↓
Planning
      ↓
Select Framework
      ↓
Load Knowledge
      ↓
Apply Workflow
      ↓
Execute with AI
      ↓
Verify Results
      ↓
Improve Framework

Notice something?

The model is only one step.

Everything else belongs to you.

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How to build your own AI harness

If you want to apply this idea, start with a simple weekly routine.

Monday: Identify recurring work

Look for tasks you repeat every week.

Tuesday: Build a reusable asset

Use your strongest AI model to create:

• a workflow
• a checklist
• a prompt library
• a decision guide
• an evaluation rubric

Wednesday–Friday: Execute

Run daily work using that reusable asset, even with a more affordable model if it performs well enough.

Friday: Review

Ask:

• What worked?
• What failed?
• What should become part of the permanent workflow?

Each week, your harness becomes stronger.

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Final thoughts

Reflect on how your organization can leverage AI effectively using this framework:

• Strengths: What internal capabilities and resources give you an advantage in building AI harnesses?
• Weaknesses: Where are the gaps in your current processes, skills, or infrastructure?
• Opportunities: What emerging AI technologies or trends can you capitalize on?
• Threats: What external factors could hinder your AI adoption or reduce its effectiveness?

The landscape of AI will continue evolving. Models will improve. Pricing will change. New leaders will emerge, and others will disappear.

These are external forces beyond our control.

What we can control is the systems we build around them.

The organizations that gain the most from AI won't necessarily be those with access to the smartest model, they'll be the ones that consistently convert intelligence into reusable assets.

That's what harnessing AI really means.

Not replacing people. Not chasing every new model.

But building a disciplined system where planning creates reusable assets, those assets guide execution, and every completed task strengthens the next one.

That's leverage.

And unlike any single model, leverage compounds.

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Key takeaways

• Separate creation from execution. Use premium AI models to design reusable frameworks, and use more cost-effective models for routine work.
• Treat AI outputs as assets. Turn successful prompts, workflows, and decision processes into documented playbooks instead of one-off conversations.
• Build your AI harness intentionally. Your prompts, memory, checklists, evaluation rubrics, and workflows are the infrastructure that creates long-term value.
• Experiment instead of assuming. Test ideas like reasoning-style extraction, measure the results, and refine your process based on evidence rather than hype.
• Invest in leverage. Every reusable asset reduces future effort, improves consistency, and increases the return on every AI interaction that follows.
• Iterate continuously. Regularly review and refine your workflows, prompts, and evaluation criteria to ensure your AI harness evolves with your needs and the capabilities of AI models.
• Focus on compounding value. Each improvement to your AI harness should make future work easier and more effective, creating a cycle of increasing returns over time.
• Document your learnings. Keep a record of what works and what doesn't, so that your team can benefit from past experiences and avoid repeating mistakes.
• Share knowledge across your team. Ensure that insights, successful strategies, and lessons learned are communicated effectively so that everyone can benefit from the collective experience.

What matters most is not having the smartest AI model, but having a system that consistently turns intelligence into reusable assets and compounds value over time.

Go Rebels! ✊🏽