System Transparency
How visible — or invisible — a system's reasoning, state, and actions are to the user.
Transparency is not a feature — it's a prerequisite for trust. Today's AI systems are among the most opaque tools humans have ever used: they process your input through billions of parameters, produce an output, and offer no explanation of what happened in between. For simple tasks, this opacity is tolerable. For agentic systems that write code, make decisions, and modify your work, opacity becomes a liability. These terms name the spectrum from total opacity to full transparency.
System Opacity
The inability to see what an AI system knows, why it made a decision, or what it did. The default state of most AI tools today.
Decision Fog
The user can see the output but not the reasoning, trade-offs, or alternatives the model considered.
Black Box Agency
An agent that acts on your behalf but won't show its work. You get results with no audit trail.
Invisible State
The system has internal state that affects behavior but is not exposed to the user.
Accountability Gap
No one — human or system — can explain why a specific output was produced.
Reasoning Visibility
The user can inspect the model's chain of thought, assumptions, and decision points.
Decision Traceability
Every output can be traced back through the reasoning, context, and data that produced it.
Open State
The system's internal state is visible and inspectable at any time. The opposite of Invisible State.
Audit Trail
A complete, immutable record of what the system did, when, why, and with what inputs.
Explainable Delegation
The agent not only does the work but can explain what it did and why in terms the user understands.