Execution Plane
This document describes the execution plane concept used by Gantral.
The execution plane defines how AI-enabled workflows are controlled, paused, and audited.
Conceptual Overview
The execution plane is a shared control layer that governs how AI participates in enterprise workflows.
In this model:
- Agents perform computation
- Gantral governs execution state
- Humans retain authority over material decisions
The execution plane is designed to be vendor-neutral and framework-agnostic.
Responsibility Boundaries
Gantral is designed to:
- Accept execution events from agents
- Evaluate control policies
- Transition execution state
- Require human input where configured
- Record decisions and context
Agents are expected to:
- Emit execution events
- Consume decisions or approvals
- Avoid embedding governance logic
This separation is intended to reduce ambiguity and conflict of interest.
Why a Separate Execution Plane
In many AI workflows, the same system both performs actions and records outcomes.
Gantral is designed to separate these concerns to support:
- Clear authority boundaries
- Independent auditability
- Policy changes without code modification
This separation is architectural, not organizational.
Integration Model
The execution plane integrates with:
- Agent frameworks (via SDKs or APIs)
- Enterprise tools (via adapters or webhooks)
- Policy configuration systems
Adapters are intended to be thin and free of business logic.
Limitations
The execution plane does not:
- Validate AI correctness
- Ensure business outcomes
- Enforce organizational policy completeness
It provides execution control mechanisms that organizations may adopt and configure.