Gantral

An open execution control plane for AI workflows

Infrastructure for enforcing human oversight, execution control, and auditability across AI-enabled processes in large organizations.

Gantral does not build AI agents.
It governs how AI execution is allowed to proceed.

Why Gantral exists

Large organizations are already using AI across the SDLC and operational workflows.

What fails at scale is not intelligence.
What fails is execution control.

In practice:

  • AI runs across many tools and teams

  • Approval and escalation are handled informally

  • Human review is assumed, not enforced

  • Execution records are fragmented or reconstructed later

  • Governance depends on discipline rather than infrastructure

This approach does not scale across hundreds of teams.

What Gantral is

Gantral is an AI Execution Control Plane.

It operates:

  • Above AI agent frameworks

  • Below enterprise processes and governance systems

Gantral provides mechanisms to:

  • Control execution state (pause, resume, override)

  • Model Human-in-the-Loop as a state transition

  • Record authority, decisions, and context

  • Produce deterministic, replayable execution records

  • Apply policy independently of agent code

Gantral focuses on execution semantics, not intelligence.

What Gantral is not

Gantral is not:

  • An AI agent builder

  • A prompt or model optimization platform

  • An end-to-end SDLC automation system

  • A replacement for existing enterprise tools

  • A system that enables self-approving AI actions

Gantral assumes:

  • Humans remain accountable

  • Material workflows require explicit human authority

  • Governance must be enforced structurally

Autonomous execution without oversight is out of scope.

HITL as execution state

Human-in-the-Loop is often treated as a UI or process concern.

Gantral treats it as execution semantics.

In Gantral:

  • Workflows explicitly pause for required human input

  • Approvals, rejections, and overrides are state transitions

  • Decision context and rationale are captured

  • Outcomes are recorded as part of execution history

HITL becomes enforceable infrastructure, not implicit behavior.

The execution plane model

Gantral introduces a shared execution plane for AI-enabled workflows.

Instead of:

  • Each team deploying isolated agents to achieve control and auditability

Gantral enables:

  • Processes defined once

  • Configuration adapted per team

  • Instances providing isolated, auditable execution

Scale is achieved through governed execution instances, not duplicated agents.

Who Gantral is for

Gantral is designed for:

  • Platform and infrastructure teams

  • AI Centers of Excellence

  • Security, risk, and compliance stakeholders

It is typically:

  • Adopted for governance, control, and auditability

  • Used for execution visibility and approvals

These roles are distinct, and Gantral is designed with that separation in mind.

Open-source core

Gantral’s execution core is open source under the Apache 2.0 license.

This allows:

  • Inspection of execution semantics

  • Independent security and compliance review

  • Long-term trust in regulated environments

Gantral follows an open-core model:

  • Trust-critical execution logic is open

  • Managed experience and enterprise tooling may be commercial

Governance and architectural decisions are documented publicly.