Trust Engineering

The key differentiator: calibrating how much to trust your agents

The question isn't "Can AI do this?"

It's "How much should I trust AI to do this?"

Trust engineering is the practice of systematically deciding how much autonomy to grant agents based on task characteristics, not gut feeling.

Trust Levels Framework

No Trust

Human does everything. AI is informational only.

Autonomy

Example: AI explains a concept, human implements entirely

Draft Mode

AI suggests, human executes. Human reviews before any action.

Autonomy

Example: AI writes code, human copies and runs it

Review Mode

AI executes, human approves. Pre-execution confirmation required.

Autonomy

Example: AI stages changes, human approves commit

Autonomous Mode

AI executes, human audits. Post-execution review.

Autonomy

Example: AI commits changes, human reviews in PR

Self-Improving

AI optimizes its own processes. Human sets boundaries.

Autonomy

Example: AI updates its own CLAUDE.md patterns based on outcomes

How to Calibrate Trust

Four factors to evaluate for any task:

Reversibility

Can this action be undone easily?

Higher Trust: Git commit (revert), draft edit (undo)

Lower Trust: Database delete, sent email, production deploy

Blast Radius

How much could go wrong?

Higher Trust: Typo in docs, style change

Lower Trust: Auth system change, financial calculation

Verification Speed

How quickly can I verify the output?

Higher Trust: 5-second visual check, automated test

Lower Trust: Requires domain expert, multi-day testing

Agent Track Record

How often has this agent succeeded at this task?

Higher Trust: Repeated similar tasks with 95%+ accuracy

Lower Trust: First time, new domain, complex edge cases

Calibration Examples

How I calibrate trust for common tasks:

Task	Trust Level	Reasoning
Fix typo in README	3	Low risk, easily verified, fully reversible
Refactor authentication flow	1	High blast radius, security-critical, needs careful review
Generate test data	2	Medium risk, should verify data quality before use
Send email to client	1	Irreversible, reputation risk, human must review
Format code with Prettier	3	Deterministic, easily reversible, well-tested tool
Write database migration	1	Potentially destructive, requires domain knowledge review

The Fundamental Principle

Trust is not binary. It's a dial, not a switch.

Start with lower trust. Increase based on evidence.

The mistake: Giving agents too much autonomy too quickly, then losing trust entirely when they fail.

The solution: Progressive trust building. Let agents earn autonomy through repeated success.

Trust engineering is how I manage my own agent systems.

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