Agentic AI Evaluation Benchmarks

Problem and constraints

A benchmark is only useful if an agent can't pass it by accident: instructions and tests that disagree, or a solution that leaks into the task, make scores meaningless.

Role and ownership

Benchmark author and evaluator: task design, environments, reference solutions, hidden tests, rubrics and quality checks.

Key engineering decisions

• Each task ships its own container, so every agent runs in the same environment and results are reproducible
• Hidden tests check behaviour, separate from what the instructions show the agent
• Business-workflow rubrics and automated quality checks alongside the tests

Tradeoffs and alternatives

• Stricter hidden tests catch more shortcuts but take longer to write and keep in sync with the instructions

Reliability, deployment, and observability

Instruction-test mismatches and solution leakage are fixed before release; adversarial and regression cases are added whenever an agent finds a gap.

Results and lessons

• Benchmarks that separate agents by real task completion, with cost and token use reported for each run

Technologies