Authority building · 9 keyword pages
Trust in AI systems is the multidimensional challenge of getting users to appropriately rely on AI agents — not too much (blind trust) and not too little (never delegating). Trust is built through consistent performance, transparent reasoning, graceful failure...
Explainability in agents is the ability to make an agent's reasoning, decisions, and actions understandable to users. This goes beyond showing chain-of-thought — it means presenting the right level of explanation for the right audience at the right time. A sal...
Observability for AI agents extends traditional software observability (logs, metrics, traces) to cover the unique aspects of agent behaviour: what the agent reasoned, which tools it called and why, how much context it consumed, where it deviated from expected...
Hallucination mitigation UX designs interfaces that account for the fact that AI agents will sometimes generate confident-sounding but incorrect information. This includes citation and source linking (so users can verify), confidence indicators (so users know...
The control vs autonomy balance is the central design tension in agentic systems: give agents too little autonomy and they're just chatbots with extra steps; give them too much and users feel out of control. The optimal balance varies by use case (low stakes =...
Debugging AI workflows is fundamentally harder than debugging traditional software because AI behaviour is non-deterministic, context-dependent, and often opaque. A workflow that worked perfectly yesterday might fail today because the agent reasoned differentl...
Agent reliability measures how consistently an AI agent produces correct, complete, and timely results across varied inputs and conditions. Reliability is the gating factor for production deployment — an agent that works 80% of the time is a demo; an agent tha...
Transparency in AI decisions means making the basis of agent choices visible and understandable to stakeholders. This includes showing which data the agent considered, what alternatives it evaluated, why it chose one path over another, and what would change if...
UX for probabilistic systems tackles the fundamental challenge that AI outputs are inherently uncertain — they're best guesses, not guaranteed answers. Traditional UX assumes deterministic behaviour (click button, get result). Probabilistic UX must communicate...