Leapter

Quickstart - Leapter Documentation

Quickstart for your first Leapter blueprint:

docs.leapter.com/get-started/...

Graphic titled “Deterministic tools for AI agents.” Steps include: identify a high value decision, define the spec, make logic visible, involve domain experts, version tools, build in Leapter, test with real data and edge cases, then export into n8n, MCP, API, or code.

Agents should orchestrate. Deterministic tools should decide.

We wrote up a practical workflow: generate a spec, review the logic visually, test boundary cases, then export into n8n, MCP, API, or code. 
www.leapter.com/post/determi...

What is Leapter - Leapter Documentation

Docs drop: start here if you want to build + verify a blueprint.
docs.leapter.com

Using Leapter with Langflow: Giving AI Agents a Logic Layer They Can Trust In this demo we combine Leapter with Langflow to automate a pizza ordering service!

A practical pattern: Langflow talks. Leapter computes the rules.

www.leapter.com/post/using-l...

Purple geometric background with a centered white quote: “For quick prototypes, speed can be enough. But when it comes to business-critical systems, speed without understanding isn’t an advantage; it’s a liability.” Leapter’s small white logo mark appears at the bottom center.

Speed is fine for prototypes.

In production, speed without understanding is a liability.

www.leapter.com/post/ai-help...

Why Agents Fail at Logic (and How to Fix It) AI agents are great at reasoning, but terrible at executing logic. Learn why they drift, where they fail, and how Leapter’s visual blueprints give them reliable, human-verified logic to run safely.

Why agents fail at logic (and what a deterministic tool changes).
www.leapter.com/post/why-age...

Introducing Leapter: The Logic Layer Every AI Agent Needs AI agents are powerful but unreliable without a logic layer. Learn how Leapter’s human-verified blueprints give agents the structure and trust they need to automate safely.

Leapter as a trust engine: rules live somewhere deterministic, not inside a model’s “best guess.”
www.leapter.com/post/introdu...

Purple-to-pink gradient graphic with the headline “Human-verifiable logic.” Three points define it: Visual (every branch and decision is exposed), Deterministic (no hidden guesses; same inputs, same output), and Executable (the blueprint is the code and the code is the blueprint; changes update the implementation). Leapter logo in the bottom-right with abstract geometric shapes on the right.

If vibe coding lets anyone generate code, the bottleneck is understanding it.

Human-verifiable logic = visual + deterministic + executable: inspect every branch, run test inputs, then publish it as a tool.
www.leapter.com/post/what-do...

What Is Leapter? Leapter reimagines the path from business intent to production-ready code with a visual, human-verifiable approach. By turning invisible logic into visible blueprints, Leapter helps teams align faster...

The hardest part of building software is shared understanding.

Leapter’s pitch: make logic visible + verifiable.
www.leapter.com/post/what-is...

White background graphic with large black headline on the left: ‘Keeping humans in the loop.’ Below, two short paragraphs read: ‘We don’t need more black boxes that ship “something” and then ask engineers to clean up the consequences. We need a glass box: logic you can see, reason about, and agree on as a team.’ and ‘Keeping humans in the loop means AI can propose solutions, but humans can verify the rules. The output stays auditable, repeatable, and collaborative, not a magic trick you’re forced to trust.’ On the right side are stacked purple-to-pink gradient squares, plus a small pixel-like icon in the bottom right.

We don’t need more black boxes that ship “something” and then make engineers clean up the consequences.

Keeping humans in the loop means the logic stays visible and testable: inspect the rules, verify the behavior, ship with confidence.

www.leapter.com/post/mind-th...

The great AI hype correction of 2025 Four ways to think about this year's reckoning

Source (MIT Technology Review): www.technologyreview.com/2025/12/15/1...

Purple gradient graphic with translucent square blocks; centered white quote reads, “It has become obvious that LLMs are not the doorway to artificial general intelligence.” A small white pixel-style Leapter logo sits at the bottom center.

Welcome to 2026: the hype correction is a feature, not a bug.

LLMs generate code fast. But logic still has to be owned, inspected, tested, repeatable.

If an agent ships what you can’t verify, you didn’t gain speed. You moved the cost into review, incidents, rework.

Link in first comment. 🔗

McKinsey’s 1-year agentic AI takeaway: focus on workflows, not shiny agents. 

Our add: agents orchestrate, deterministic tools decide. If the rule matters, you should be able to inspect what ran. www.mckinsey.com/capabilities...

Using Leapter with Langflow: Giving AI Agents a Logic Layer They Can Trust In this demo we combine Leapter with Langflow to automate a pizza ordering service!

Agents should talk. Tools should decide.

We demoed @langflow.org + Leapter with a pizza-ordering agent: Langflow orchestrates, Leapter computes deterministic pricing via MCP.

🍕Playful example, production pattern.
www.leapter.com/post/using-l...

Why Agents Fail at Logic (and How to Fix It) AI agents are great at reasoning, but terrible at executing logic. Learn why they drift, where they fail, and how Leapter’s visual blueprints give them reliable, human-verified logic to run safely.

Agents aren’t failing because they can’t “reason.”

They fail because they can’t reason consistently—and deterministic logic doesn’t tolerate vibes. 

Why Agents Fail at Logic (and how to fix it):
www.leapter.com/post/why-age...

“You can’t trust what you didn’t validate. And you shouldn’t have to.”

AI-generated code can run and still be wrong. When logic is opaque, teams pay the review tax.

Leapter makes logic visible first — so humans can verify it before it becomes code.

www.leapter.com/post/mind-th...

YouTube video by Leapter Black Box or Glass Box? The only way to reliable Enterprise AI. | Leapter

Can you trust your AI agent with pricing, risk scores, or approvals?

In this new video, Marlene Schlegel shows how Leapter’s Logic Server gives agents deterministic tools they can call instead of guessing from a prompt.

🎥 www.youtube.com/watch?v=oggt...

YouTube video by AI with Lena Hall Build Deterministic AI Tools for Reliable AI Agents: Leapter + n8n Demo

Agents can’t “vibe” their way through pricing or risk rules.

In this video, Lena Hall uses Leapter + @n8n.io to keep logic visual, verifiable, and deterministic while the agent handles execution.

🎥 www.youtube.com/watch?v=6nLJ...

Most AI tools generate code. The real problem is agreeing on the logic before the code exists.

Leapter makes that logic visible so teams can trust what they ship.

McKinsey’s latest AI report makes a clear point: AI inaccuracy is already creating real problems for teams.

This is exactly why we built Leapter around visual, human-verifiable logic. If you can’t see the logic, you can’t trust it.

Report → archive.ph/aj7mb

Introducing Leapter: The Logic Layer Every AI Agent Needs AI agents are powerful but unreliable without a logic layer. Learn how Leapter’s human-verified blueprints give agents the structure and trust they need to automate safely.

People talk a lot about agent “intelligence," and not enough about agent consistency.

Leapter adds determinism with a human-verified logic layer.

Revisiting this post → www.leapter.com/post/introdu...

YouTube video by AI with Lena Hall Build Deterministic AI Tools for Reliable AI Agents: Leapter + n8n Demo

AI agents improvise. Your tools shouldn’t.

Here’s how to build deterministic, human-verified tools without writing code, test the logic visually, and export them straight into #n8n.

🎥 www.youtube.com/watch?v=6nLJ...

Introducing Leapter: The Logic Layer Every AI Agent Needs AI agents are powerful but unreliable without a logic layer. Learn how Leapter’s human-verified blueprints give agents the structure and trust they need to automate safely.

AI agents are multiplying, but reliability hasn’t caught up. They reason probabilistically, not deterministically.

Leapter adds the missing layer: human-verified logic agents execute rather than guess.

Why it matters → www.leapter.com/post/introdu...

YouTube video by Leapter Leapter @AIForTheRestOfUs 2025 in London ...

Just a little glimpse of Leapter in London. 🇬🇧

Big ideas, small talk, and a question we keep coming back to:

What if we built a language both humans and AI could understand?

🎥 www.youtube.com/watch?v=Iuyq...

What Is Leapter? - Leapter Leapter reimagines how software gets built—turning business intent into visual, auditable logic that’s generated by AI and trusted by humans.

#ICYMI: Turning business intent into trusted software shouldn’t require a dozen handoffs.

Leapter makes logic visual, auditable, and human-verifiable so teams ship faster and smarter.

🔗 www.leapter.com/what-is-leap...

What Is Leapter? - Leapter Leapter reimagines how software gets built—turning business intent into visual, auditable logic that’s generated by AI and trusted by humans.

Software often breaks in translation.
The business explains. Product rewrites. Engineering interprets.

Leapter fixes the loop — turning business intent into visual, human-verifiable blueprints everyone can trust.

🔗 www.leapter.com/what-is-leap...

Mind the Gap: Why We Don’t Trust AI-Generated Code (Yet) - Leapter AI coding tools are exploding in popularity. Everywhere you look, there’s a new promise: faster development, smarter automation, and no more late nights squinting at your terminal. For developers, it ...

AI tools can write code—but can you trust it?

Speed means nothing if you can’t see the logic.

Leapter makes AI output visible, auditable, and human-verifiable—so teams move fast and trust what they ship.

www.leapter.com/mind-the-gap...

A close-up photo of a green frog perched on a leaf. The frog is looking directly at the camera, symbolizing a clear leap forward — used here as a visual metaphor for Leapter’s mission to bring clarity and trust into software development.

Big leaps in software come from clarity, not just code. 🐸

Leapter makes business logic visual & verifiable—so teams can trust what they ship.

The myth of AI code gen:
🚫 Hallucinations aren’t going away
🚫 “Faster” now means slower later in validation

At Leapter, trust comes first: visible, verifiable blueprints → production code.

🎥 Oliver Welte explains.

What if “human in the loop” meant the business user—not just the developer?

Leapter makes it possible for domain experts to validate system logic early. Trust, built in.