The AI Taxonomy.
Terms  /  Human-Agent Relationship  /  Calibrated Trust
07 · Human-Agent Relationship

Calibrated Trust

The user accurately understands what the agent can and can't do, and delegates accordingly.

Calibrated Trust is the healthy middle ground between Trust Without Verification (too much trust) and Confidence Decay (too little trust). A user with Calibrated Trust knows that the agent is excellent at generating boilerplate code and unreliable at complex architectural decisions. They delegate the former with minimal review and scrutinize the latter carefully. Calibrated Trust is built through experience and transparency — the user has seen enough of the agent's work to know where it excels and where it fails.

Example
A developer has worked with their AI agent for three months and has developed Calibrated Trust. They know: the agent writes clean, correct CRUD operations (low review needed), it handles complex state management well but sometimes misses edge cases (medium review needed), and it makes poor security decisions (high review needed). They delegate accordingly, spending their review time where it matters most. They're neither blindly trusting nor paranoidly verifying — they're calibrated.

More in Human-Agent Relationship

Human-Code Divergence

The gap created between a user and their codebase when an agent mediates all development.

Cognitive Load

The mental burden placed on the human when working with AI systems.

Agency Erosion

Gradual loss of human decision-making authority as agents take over more workflow without checkpoints.

Skill Atrophy

Decay of human technical skills from prolonged delegation to agents.

Automation Dependency

Reliance on agents to the point where the human can't perform the task without them.

Trust Without Verification

Accepting agent output without review — a precondition for Execution Hallucination going undetected.

Confidence Decay

The gradual erosion of user trust as a system repeatedly loses context, hallucinates, or fails to follow through.

Capability Illusion

The user believes the AI can do more than it actually can, leading to over-delegation and undetected failures.