Agent Evaluation & Benchmarking
Evaluation environments that reveal where agents actually fail.
We design realistic tasks, adversarial scenarios, and benchmark environments for teams measuring autonomous agent capabilities.
Strong benchmarks need more than hard prompts.
Agent evaluations need realistic environments, clear success criteria, shortcut resistance, robust rubrics, and failure analysis. Otherwise agents pass for the wrong reasons or fail in ways the benchmark cannot diagnose.
Tasks with unintended shortcuts
Rubrics that miss important failure modes
Environments that are too brittle or too easy
Agents passing through shallow pattern matching
Unclear distinction between model failure and task failure
Poor coverage of multi-step planning, tool use, and recovery
Services
Realistic worlds, stronger rubrics, better failure coverage.
Evaluation environment design
Build realistic worlds and workflows for testing autonomous agents.
Adversarial task construction
Design tasks that expose weaknesses in planning, tool use, reasoning, recovery, and robustness.
Benchmark hardening
Remove shortcuts, clarify rubrics, and improve task reliability.
Failure-mode analysis
Analyse where agents fail and turn those failures into better eval coverage.
Capability assessment
Create structured test suites for specific agent capabilities and domains.
Regression suites
Build repeatable eval packs to compare agents, prompts, tools, and model versions.
Relevant systems
For teams building and measuring autonomous agents.
This work is especially relevant for teams building coding agents, data agents, browser agents, terminal agents, workflow agents, and research agents.
Need realistic agent evals? Let’s talk.Agent evaluation project
Need realistic agent evals? Let’s talk.
Tell us what you are building, evaluating, analysing, or trying to automate. We will help choose the right service path.
Prefer email? drew@agent-reliability.com