Introducing Leapter: The Logic Layer Every AI Agent Needs
- Mandy Moore
- 4 days ago
- 3 min read
Why reliable automation starts with human-verified logic
Everywhere you look, AI agents are multiplying.
They schedule meetings, write emails, run tests, deploy apps, and even make API calls.
But ask one to make a nuanced business decision or execute a conditional workflow with multiple edge cases or calculations, and you’ll hit the same wall:
The logic doesn’t hold.
The agent's response is subtly different each time; it improvises or hallucinates its own rules.
And no matter how specific the prompt, the agent eventually makes a mistake. And that’s expected. Large Language Models are non-deterministic; they weren’t designed for this.
We could do so much more with AI Agents if we could trust them to execute deterministic logic consistently and reliably. Most AI agents are missing one critical piece of infrastructure:
A logic layer—a foundation we can trust.
Agents don’t fail at language. They fail at logic.
Large Language Models are excellent at reasoning in natural language; they can plan, analyse, and facilitate.
They can make choices and decisions based on probabilistic reasoning. They can get the answer right. However, they can also inevitably get it wrong.
When you string multiple steps together—“if this, then that, unless this”—each step is a roll of the dice. Errors in reasoning, even a small percentage of the time, compound to significantly impact the reliability of a workflow.
An average success rate of 95% across a 5-step process yields an overall success rate of 77%. No business I know would accept a failure rate like this.
Then what? You lose trust in your Agents.
They demo well but they don’t get the job done.
In production, with real users, reliability beats creativity every time.
What a logic layer actually does
A logic layer gives AI agents something they don’t inherently have: determinism.
A toolkit to use when a task needs to be specific and not probabilistic. AI Agents with precision.
Think of it as the executable blueprint that sits between your business and your automation. A new way of building Agentic Automation.
It provides:
Precision – Each task executed using Leapter is executed correctly every time.
Explainability – You can trace why an outcome occurred.
Auditability – Nothing runs that hasn’t been verified.
Safety – Agents do what agents do best with access to the right tools.
Instead of improvising logic from a prompt, the agent simply calls Leapter as a trusted tool.
Why code alone isn’t enough
You could hard-code logic manually, of course. Many teams do - using a custom code node or similar.
But that approach brings you right back to the old software bottlenecks:
Waiting for a developer to make small changes to your business logic
Edge cases that weren’t captured in the requirements
Building and maintaining a catalogue of custom tools for your business
AI-generated code doesn’t solve this either—it’s fast, but opaque.
You still need to read, review, and debug it line by line to know what it really does. Your domain expert can’t own that code.
What’s missing is a shared, human-verifiable logic layer—one that lives between the code and Agent Platform.
Enter Leapter: the logic layer for AI-native systems
Leapter was built for this exact purpose.
It lets humans design, visualize, and validate system logic.
We uncovered a need. It’s a new problem that we’re only just starting to understand.
Anyone can build an AI Agent. It’s just natural language. But there was no way to build custom Agent Tools unless you were a developer. Until now.
Each flow, condition, and data path is explicit, reviewable, and exportable as an executable model.
Then, when an AI agent runs, it doesn’t have to guess the price of a product or order, it’s not calculating risk scores in loan applications, it’s not using a large language model to calculate tax or credit scores. It has the right tool for the job, the right tool for your business.
It simply executes the verified logic you’ve already approved.
The result:
Automation that’s transparent, not magical.
Agents that act with precision and reliability.
Teams that can finally trust their AI workflows.
From intent to trust
In the next wave of AI, trust will matter more than novelty.
We don’t need agents that get things right most of the time.
We need agents that act correctly. Every time.
That’s why every AI agent needs a logic layer—a place where business rules, human understanding and machine execution meet.
At Leapter, that’s exactly what we’re building:
a visual, auditable foundation that makes AI automation explainable, reliable, and truly collaborative.
Because when logic is visible, trust becomes automatic.