AI Evaluation.

Measure real-world capabilities

Lab scores do not say what the system can do on the actual job.

Evaluate agentic capabilities

Tests never check what the system does once it can use tools and take steps.

Test long-horizon tasks

Short tests miss failures that only appear after many steps.

Identify emerging capabilities

New abilities show up in use before anyone has tested them.

Test dangerous capabilities

Nobody checks whether the system can do serious harm if it is asked or steered.

Evaluate refusal behaviour

Refusals are inconsistent, so blocked requests sometimes go through.

Test boundary conditions

The system is untested at the edge of its allowed use.

Identify unexpected failure modes

Failures outside the written test plan reach users first.

Run adversarial testing

The system is only tested with friendly inputs.

Run misuse testing

Likely misuse paths are never tried before release.

Simulate agent attacks

Attacks that use the agent tools are not rehearsed.

Test real-world scenarios

Tests use toy tasks instead of the situations users actually face.

Develop meaningful benchmarks

Existing scores do not measure the decision that matters.

Avoid benchmark gaming

The system is tuned to the test and weaker on the real task.

Evaluate domain-specific performance

A general score hides failure in the domain where the system will be used.

Compare systems under equivalent conditions

Comparisons mix different data, prompts, or tools and are still treated as equal.

Monitor deployed systems

Behaviour after launch is not measured.

Detect capability drift

What the system can do changes, and the last evaluation is treated as current.

Detect changing failure modes

New ways of failing appear after release and stay invisible.

Re-evaluate systems after updates

A model or prompt update ships without a new evaluation.