Agentic Experience Design (AXD) is the discipline for designing trust-governed relationships between humans and autonomous AI systems. Founded in September 2024 by Tony Wood in Manchester, United Kingdom, AXD addresses how humans delegate, calibrate, observe, interrupt, and recover trust in agentic AI.
| Dimension | Traditional UX | Agentic Experience Design (AXD) |
|---|---|---|
| Primary material | Attention and affordance | Trust and delegation |
| User state | Present, navigating | Absent, delegating |
| Design output | Screens and interfaces | Outcomes and constraints |
| Temporal model | Session-based | Relationship-based |
| Success metric | Task completion | Trust calibration |
AI agent oversight is the designed system of observability, monitoring, intervention, and governance that enables humans to maintain meaningful control over autonomous AI agents. It is not surveillance (watching everything in real time) nor post-hoc audit (reviewing after the fact). Effective oversight provides the right information, to the right people, at the right time, enabling informed intervention when necessary while preserving the efficiency gains of autonomy.
The oversight paradox is the fundamental tension between autonomy and control: the more autonomous an agent becomes, the more valuable it is - but the harder it is to oversee. Full oversight defeats the purpose of autonomy. Zero oversight creates unacceptable risk. The AXD Institute resolves this through designed oversight that is inversely proportional to routine and directly proportional to consequence - minimal monitoring for routine tasks, intensive monitoring for novel or high-consequence a
The AXD Institute defines three oversight modes: Ambient Oversight (passive anomaly monitoring for routine, low-consequence actions), Periodic Review (regular summary review for moderate-consequence actions), and Active Supervision (real-time monitoring with intervention capability for high-consequence or novel situations). These modes exist on a continuum, and the oversight system should transition dynamically between them based on the situation.
Observability for autonomous agents requires four components: decision logging (recording what the agent decided and why), boundary monitoring (tracking proximity to authority limits), outcome tracking (connecting agent decisions to real-world results), and anomaly detection (automatically identifying behaviours that deviate from established patterns). Together, these components provide the visibility that makes meaningful human oversight possible.
AI agent oversight governance requires: accountability structures (clearly identified humans responsible for each agent), oversight standards (minimum monitoring requirements for each activity type), escalation protocols (clear paths from oversight findings to action), regulatory compliance (mapping legal requirements to specific oversight mechanisms), and continuous improvement (regular reviews of oversight effectiveness). These frameworks ensure consistent, effective oversight across all deplo
AI agent oversight faces a fundamental paradox: the more autonomous an agent becomes, the more valuable it is - but the harder it is to oversee. An agent that requires constant human monitoring provides little benefit over doing the task yourself. An agent that operates without any oversight creates unacceptable risk. The design challenge is to create oversight systems that provide meaningful human control without eliminating the efficiency gains of autonomy. This paradox cannot be resolved by choosing one extreme or the other. Full oversight (monitoring every agent action in real time) defeats the purpose of autonomy - the human is effectively doing the work themselves, with the agent as an intermediary. Zero oversight (letting the agent operate without any monitoring) creates unacceptable risk - the human has no way to detect errors, boundary violations, or drift until consequences materialise. The solution lies in The AXD Institute's approach to the oversight paradox is based on the principle that This means that AI agent oversight is not a static system - it is a dynamic, adaptive architecture that responds to the nature of the agent's current activity. The same agent may require minimal oversight when performing routine tasks and intensive oversight when encountering novel situations. Designing this adaptive oversight is one of the most important challenges in The AXD Institute defines three modes of AI agent oversight, each appropriate for different situations and autonomy levels: The agent operates independently, and the oversight system passively monitors for anomalies. The human is not actively watching - they are informed only when the system detects something that requires attention. Ambient oversight is appropriate for routine, low-consequence agent actions where the agent has demonstrated consistent competence. The design challenge is building anomaly detection that is sensitive enough to catch genuine problems but not so sensitive that it generates con