Agent Evaluation and Reliable AI Systems

Translate product promises into measurable criteria.

Evidence: A matrix maps every important promise to an observable test.

Build representative, adversarial and withheld scenario sets.

Evidence: Dataset provenance and leakage controls are documented.

Inspect intermediate tool calls and state transitions.

Evidence: A trace review identifies the first causal failure point.

Validate schemas, citations, permissions and business invariants.

Evidence: Seeded rule violations are reliably detected.

Design a judge rubric and compare against human labels.

Evidence: Agreement and disagreement examples are reported without claiming certainty.

Repeat runs and estimate variability across settings.

Evidence: A report separates average score from failure frequency.

Simulate tool timeouts, stale results and malformed responses.

Evidence: The service reaches a controlled recovery or escalation state.

Test prompt injection, data leakage and unauthorised tool use.

Evidence: Every critical test has a documented expected safe outcome.

Measure distributions and define release thresholds.

Evidence: A load sample records p50, p95 and per-task cost.

Compare candidate and baseline on a fixed evaluation set.

Evidence: A regression blocks release with an actionable report.

Convert reviewed incidents into tests without exposing user data.

Evidence: A sanitised incident becomes a reproducible regression case.

Present evidence, residual risk and rollback steps to reviewers.

Evidence: A reviewer can reproduce the release decision from stored evidence.