🤖 AI Summary
This study addresses the challenge of deploying agentic AI in regulated environments, where existing approaches lack a systematic design framework that jointly accounts for autonomy and agency, often failing to balance compliance, auditability, and error correction. The work introduces the first unified model of these two dimensions, defining a two-dimensional hierarchical design space with five operational levels each. It proposes six architectural strategies—checkpoints, escalation mechanisms, multi-agent delegation, tool provisioning, tool sandboxing, and write staging—to enable flexible system configuration under real-world regulatory constraints. Validated through public-sector case studies, the framework establishes a shared terminology and actionable design guidelines, facilitating interpretable, controllable, and compliant AI deployment amid evolving model capabilities and tool fidelity.
📝 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.