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What Is Gantral

Gantral is an open-source AI Execution Control Plane.

It standardizes how AI-enabled workflows are executed, paused, escalated, approved, overridden, and audited across teams and systems.

Gantral exists to solve a specific problem faced by large organizations:

AI adoption breaks execution, control, and accountability — not model quality.

As AI tools spread across the software development lifecycle (SDLC) and operational workflows, organizations lose a consistent way to answer fundamental questions:

  • What ran?
  • Under whose authority?
  • With what configuration?
  • What human approved or overrode the outcome?
  • Can this decision be replayed and audited?

Gantral provides infrastructure-level mechanisms to record and surface answers to these questions.

The Core Idea​

Gantral introduces a shared execution plane that sits:

  • Above AI agent frameworks (LangChain, CrewAI, Vellum, custom agents)
  • Below enterprise processes (SDLC, incident management, governance)

Agents perform computation.
Gantral governs execution.

This separation is designed to prevent AI-driven execution from advancing past governed states without explicit authorization.

What Gantral Owns​

Gantral owns execution semantics, not agent intelligence.

Specifically, Gantral provides:

  • A deterministic execution state machine
  • Human-in-the-Loop (HITL) as a first-class state transition
  • Instance-level isolation for audit, cost, and accountability
  • Declarative control policies (materiality, escalation, authority)
  • Immutable execution records with replay capability

Gantral is intentionally boring, predictable, and auditable.

What Gantral Enables​

With Gantral, organizations can:

  • Standardize HITL across AI workflows
  • Scale AI usage across hundreds of teams without duplicating agents
  • Enforce governance policies without modifying agent code
  • Produce audit-ready execution records by default
  • Separate experimentation (agents) from accountability (execution)

Gantral does not make AI more powerful.

It makes AI safe, governable, and operable at scale.

Mental Models​

Gantral can be understood as:

  • Kubernetes — but for AI execution semantics, not containers
  • Terraform — but for AI process control, not infrastructure
  • ServiceNow — but for AI execution governance, not ITSM

These analogies are conceptual and do not imply feature parity, compatibility, or equivalence.

Gantral defines a new control layer specific to AI-enabled workflows.