What Does “Human-Verifiable Logic” Actually Look Like?
- Mandy Lee
- 7 days ago
- 5 min read
Why clarity matters more than code when building trustworthy AI systems.
Back in 2011, Mark Andreessen said, “Software is eating the world,” but today it feels like AI is eating software. With the rise of “vibe coding,” making it possible for anyone to create software, we find ourselves in a new world where understanding the software is the new bottleneck.
Similarly, we hear day in and day out that AI agents will automate away all our mundane, repetitive work. And anyone can create an agent — it’s just a natural language prompt after all. If anyone can create an agent, how do they ensure the agent is reliable?
How can we make sure the human, whether a developer or novice, can build reliable agents? What tools do they need to understand the inevitable code that’s involved?
Picture a common scenario:
You’re using AI agents to automate part of your business. The requirements are clear, but the agent can’t execute the process consistently. LLMs aren’t built for deterministic decision making.
Your agent needs deterministic steps to complete the work for you. You need a custom tool, created specifically for your business and your processes.
But you’re not a developer. Do you wait for a developer to help you? Do you use one of the popular “vibe coding” assistants? Neither gets the job done quickly if you want to work independently and ship with confidence.
At Leapter, we believe the missing piece isn’t more powerful AI. It’s a clearer way for humans to see and verify logic, giving everyone the ability to build tools for their AI Agents.
That’s what we mean by human-verifiable logic, and in this post, we’ll show what it actually looks like.
What you can see and do in Leapter
In Leapter, “human-verifiable” is not a philosophy. It’s a set of concrete interactions and features in our platform:
A human-readable diagram in the centre that shows the logic as a blueprint you can inspect path by path
An AI chat tool where you can describe changes in natural language and immediately see the update reflected in the diagram
A code view in the top right that stays in sync with the diagram, so you can inspect the underlying code whenever you want
Inputs and outputs are defined in the left-hand panel, so it’s always clear what the logic expects and what it returns
A big green Play button that lets you run test inputs through the logic, validate edge cases, and confirm behavior before you publish anything
That combination is what we mean by human-verifiable logic: you can see it, change it, and test it without needing to read the code.
What “human-verifiable logic” means
For us, it comes down to three things:
1. Visual
You can see how the system behaves without interpreting code or prompts.
Every branch, decision, condition, and fallback is exposed.
2. Deterministic
No hidden model guesses, no emergent behaviors.
If the logic says “do X,” the system does X, every time.
3. Executable
The Leapter blueprint is executable. The blueprint is the code, and the code is the blueprint. Whatever changes you make using the visual tools are driving corresponding changes in the underlying code.
We believe that trust is built on understanding, and that we can improve understanding with human-centric visualisations. We shared this in our founding story.
Let’s look at what that actually means for teams
From natural language to verified tool, without needing to code!
A simple example that would normally hide a dozen subtle mistakes.
Imagine a team needs to implement a basic business rule:
“If a customer places an order over €500 and they’re a new customer, apply a 10% discount. Otherwise, apply a 5% discount.”
This is straightforward for a human to reason about. But hand it to an AI agent, and you won’t get the correct result consistently.
In an AI agent
An AI agent can orchestrate the workflow, but it shouldn’t be responsible for the business logic itself. This is the point where you give it a deterministic tool that encodes the rules.
Without that tool, the agent will still have to “decide” what the rule means, and it can easily get details like these wrong:
What counts as “new”?
Does €500 include tax?
Do we apply rounding rules?
Is the discount applied per item or per order?
What happens when the customer is right at €500? (€500.00 vs €499.99)
These issues usually aren’t bugs in the traditional sense. They’re gaps in the requirement that force someone, or something, to make assumptions.
In natural language
The rule looks clear. In practice, it leaves room for interpretation.
In a Leapter blueprint
Here’s what changes. Instead of relying on handoffs, the blueprint becomes a shared workspace that the team can iterate on directly.
You can describe changes in natural language using the AI chat tool, then immediately see the update reflected in the diagram.
You can run test scenarios through the logic, explore edge cases, and confirm the behavior using defined inputs and outputs.
And if you do want to inspect the implementation, the generated code stays in sync with the blueprint, but you don’t have to read it to validate what the system does.
The goal is simple: make iteration and verification fast enough that “check the logic” becomes the default, not an optional step that only happens in code review.
Suddenly, ambiguous phrases like “new customer” or “over €500” become explicit logic nodes with definitions attached. A human verifies each branch. Leapter publishes the logic as a tool after the logic has been tested and verified.
No guesswork. No translation tax. This is the moment teams say, “Now I trust my agent to follow the rules consistently.”
This is the glass-box alternative
Most AI tools ask you to trust what you can’t see.
Leapter does the opposite: it shows you everything that matters and invites you to shape it.
If humans can’t verify the logic, they can’t trust the system, and if they can’t trust the system, they won’t use it, no matter how impressive the demo may be.
This focus on understanding as the foundation of trust is core to why Leapter exists, and something we explored in our story behind the product.
Human-verifiable logic is how you close the trust gap.
Now it's your turn
Anyone can build with Leapter. Explore one of our examples or start creating your own Blueprints today!
Clarity before code. That’s the Leapter way.