A Control Model for Hands-Free Business Operations with Agentic OS
Armalo Labs
Key Finding
Frames hands-free autonomy as evidence-bearing delegation rather than human disappearance.
Abstract
Defines hands-free business operation as bounded autonomy over mission packets, governed tool access, proof receipts, trust movement, and human escalation thresholds.
This paper proposes a control model for hands-free business operations powered by an Agentic OS. The model defines autonomy as a bounded state granted to a named agent for a named mission, constrained by tool policy, spend limits, customer-impact boundaries, memory provenance, proof requirements, and escalation thresholds.
Research Basis
The model draws on ReAct for interleaved reasoning and acting, NIST AI RMF for lifecycle governance, and agent evaluation critiques that warn against capability claims without rigorous task evidence.
Model
Object
Definition
Evidence
Mission packet
Business outcome delegated to an agent
Objective, non-goals, owner, acceptance criteria
Capability grant
Tool or action authority
Scope, expiry, approval class
Proof receipt
Replayable action evidence
Source, tool output, decision, result
Trust movement
Change in future autonomy
Grant, hold, narrow, pause, escalate
Cite this work
Armalo Labs (2026). A Control Model for Hands-Free Business Operations with Agentic OS. Armalo Labs Technical Series, Armalo AI. https://www.armalo.ai/labs/research/hands-free-business-agentic-os-control-model
Armalo Labs Technical Series · ISSN pending
Explore the trust stack behind the research
These papers are built from the same trust questions Armalo is turning into product surfaces: pacts, trust oracles, attestations, and runtime evidence.
Business operators will trust autonomous management faster when autonomy is represented as mission-specific authority with receipts and downgrade rules instead of broad agent access.
Limitations
The model does not claim universal full autonomy. It identifies which loops are candidates for hands-free operation and which remain human-reviewed until stronger evidence exists.
Trust Lab Peer Review Matrix: Positioning Runtime Trust Research Beside Model Research