Governance for AI agents

Your agents stay flexible. Your decisions stay deterministic.

Leapter is the deterministic policy layer for agent stacks. The agent handles the flexible parts — interpreting input, gathering context, choosing the next step. When a real decision must be made, it calls an approved Blueprint — the business logic itself, made visual and executable. Same input, same output, traced.

AI agents that don't guess. Governed. Auditable.

Leapter sits beside your agent stack. The agent handles the flexible parts — interpreting input, gathering context, choosing actions. When a real decision needs to be made, the agent calls a Leapter Blueprint. Same input, same output, every time. Verifiable, traceable, signed off by a human before deployment. The agentic part is design time. The live decision is deterministic.

The result: agentic flexibility with enterprise governance. No drift. No hallucinated rules. No gaps when the auditor asks why.

One decision, end to end

The request arrives

The agent interprets it, pulls the documents, gathers the applicant’s context — the work agents are genuinely good at.

The decision point

The agent calls the approved Blueprint as a tool — through MCP, the agent integration standard, or a REST API.

The Blueprint decides

The approved version of the logic runs deterministically. No prompt, no temperature, no model in the decision.

The trace is recorded

Inputs, the branches that fired, the output, and the Blueprint version — linked to the run, replayable later.

The agent continues

It explains the outcome, drafts the letter, routes the case. Flexibility resumes after the decision — not inside it.

Agent alone vs agent + Leapter

	Agent alone	Agent + Leapter
Decision logic	Inside the LLM, unverifiable	In a Blueprint, inspectable by the rule owner
Reproducibility	Varies with prompt, model, and context	Same input, same output, every time
Who approves the rule	No one — it's emergent	The domain expert who owns it
Audit trail	Output logs only	Intent, version, and execution linked
Regulatory scrutiny	Hard to evidence	Governance evidence built in: versions, traces, human sign-off
Integration	Custom code per call	Native MCP — every Blueprint is a callable tool

From policy to production in 3 steps

AI helps you build it. No AI when it runs.

01. Co-author the logic

Describe the rule in plain language, or drop in an existing policy document. Leapter turns your description into a Blueprint. Every condition, every branch, every threshold laid out.

02. Review and refine

See how each decision is made. Change a threshold, reorder a branch, adjust the logic directly. No code to read. No developer queue.

03. Deploy with deterministic execution

The approved Blueprint is exactly what runs. Same input, same output. No LLM in the decision path.

Use cases

Built for decisions where agents need exact answers

Lending decisions

Approve or deny based on your credit policy. Not the model’s interpretation of your credit policy. The agent assembles the application; the Blueprint applies the policy.

Claims and warranty adjudication

Approve, deny, or escalate based on your actual policy. Not a probabilistic guess that varies by the hour.

KYC and AML checks

KYC, anti-money laundering, sanctions screening. Rules that regulators will audit — and that need to produce the same answer every time.

Eligibility decisions

Coverage, benefits, and product eligibility. The agent can gather the applicant’s situation and explain the outcome — but the published policy, not the conversation, decides who qualifies.

What the auditor sees

No tool makes you compliant — no honest vendor claims otherwise. What Leapter gives your team is evidence. Automated decisions face growing regulatory scrutiny; when yours come under it, this is on file:

The approved version

Which Blueprint version was live for any given decision, and the named human who signed it off before deployment.

The full trace

Inputs, the branches that fired, the output — recorded for every run and replayable step by step.

The change history

Who changed what, when, and the test cases that ran before each approval.

Know exactly why every decision was made. Full version history, full execution trace. Evidence regulators can read.

Fits the agent stack you already run

Leapter does not replace your agent framework, your orchestration, or your models. Any agent that can call tools can call a Blueprint. Engineering gets a stable interface; the agent gets one tool that never improvises.

Where it runs

The first question your architecture and compliance reviews will ask, answered plainly:

Built for the people who answer for the agents

CIOs & heads of AI

You’re being asked to deploy agents at scale without losing control. The agentic part is design time; the live decision is deterministic. Leapter is the deterministic layer between LLM reasoning and consequential execution, so your agents stay fast, auditable, and inside policy.

Heads of compliance & risk

Automated decisions face growing regulatory scrutiny, and you answer for them. Leapter gives you a visual audit trail your regulators can read, without waiting on engineering to explain what the model did.

Leapter is built by founders with decades of experience with visual business rules and regulated decisioning — the decision layer is the part of the stack they have built before.

Design Partner Briefing

For enterprise teams

Book a 30-minute briefing. Bring one example from your business: a decision, rule set, workflow, calculation, or policy. We’ll show how it becomes visual logic your experts can read, test, and approve.

Investor Materials

For investors

Request the investor deck and technical brief covering market size, EU AI Act tailwinds, product architecture, and the design-partner path to traction.

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Design partners & investors

We work with a small number of design partners and are selectively expanding our investor circle.