Independent benchmarks for real-world agent capabilities
Deaimer develops reproducible benchmarks that measure whether AI agents can complete difficult technical work — not simply produce convincing responses.
Principles behind how we measure agents.
Realistic work, not artificial puzzles
Tasks are modeled on real engineering work rather than synthetic exercises designed to be easy to grade.
Reproducible, resettable environments
Every environment resets deterministically, so results can be verified and repeated independently.
Objective, auditable evaluation
Grading relies on verifiable checks and recorded evidence, not subjective judgment calls.
Resistant to shortcuts & leakage
Tasks undergo adversarial review to close off shortcuts, data leakage, and reward-hacking paths.
A growing set of agent benchmarks.
Each benchmark in Deaimer's portfolio targets a distinct set of agent capabilities, built and validated using the same reproducibility and integrity standards.
An audited benchmark for whether AI agents can safely recover unfamiliar, production-like broken systems — evaluating functional recovery alongside state safety, durability, integrity and evidence quality.
Additional benchmarks in development
Deaimer's benchmark portfolio is expanding into additional agent capabilities and specialist domains. Further releases will be announced after validation.
Evaluate an agent or validate your tasks.
Discuss a private ColdStart evaluation, or have Deaimer independently validate your existing agent task suite.