Agent Tester

The service supports testing across diverse AI setups such as standalone language models, orchestrated multi-agent pipelines, automation tools, and stateful long-context interactions. Evaluations measure functional correctness, robustness, safety alignment, and evidence handling while checking against a taxonomy of ten specific failure types including hallucination, instruction drift, and multi-agent handoff errors. Reports include real execution audits drawn from logs or traces, design reviews of proposed architectures, and regression comparisons between versions. Each assessment highlights critical issues like context loss or weak guardrails and supplies concrete recommendations to close performance gaps. Access begins with individual tests at a low fixed price, with bundled options available for iterative optimization or large-scale agent fleets. All results focus on revealing discrepancies between intended design and actual behavior to support more reliable AI development.