Autonomy and Agency in Agentic AI: Architectural Tactics for Regulated Contexts

TL;DR

This paper introduces a two-dimensional design space for agentic AI, organizing agency and autonomy into five levels, and proposes architectural tactics to navigate compliance and oversight in regulated contexts.

Contribution

It presents a novel joint framework for agency and autonomy levels, along with six architectural tactics and deployment parameters for principled AI system design.

Findings

A structured design space with five levels for agency and autonomy.

Six architectural tactics for adjusting AI deployment within the design space.

Analysis of five deployment parameters influencing achievable configurations.

Abstract

Deploying agentic AI in regulated contexts requires principled reasoning about two design dimensions: agency (what the system can do) and autonomy (how much it acts without human involvement). Though often treated independently, they are coupled: at higher autonomy, human error correction is less available, so reliable operation requires constraining agency accordingly; compliance requirements reinforce this by mandating human involvement as action consequences grow. Yet no established approach addresses them jointly, leaving practitioners without a principled basis for reasoning about oversight, action consequences, and error correction. This work introduces a two-dimensional design space in which both dimensions are organised into five operational levels, making the coupling explicit and navigable. Autonomy ranges from human-commanded operation (L1) to fully autonomous monitoring (L5); agency ranges from reasoning over supplied context (L1) to committed writes to authoritative records (L5). Building on this space, we propose six architectural tactics--checkpoints, escalation, multi-agent delegation, tool provisioning, tool fencing, and write staging--for adjusting a deployment's position within it. The tactics are grounded in two worked examples from public-sector contexts, illustrating how they apply under realistic compliance constraints. We further examine five deployment parameters--model capability, agent architecture, tool fidelity, workflow bottlenecks, and evaluation--that shape what is achievable at any configuration independently of agency and autonomy. Together, the design space, tactics, and deployment parameters provide a shared vocabulary for principled, compliance-aware agentic AI design in which responsibility, auditability, and reversibility are explicit design considerations rather than properties that must be retrofitted after deployment.