Giskard vs Google Cloud Agent Evaluation

Giskard provides tools for assessing AI agents before production, with coverage for prompt injection, data disclosure, inappropriate content, hallucinations, contradictions, and omissions. Its Hub performs black-box testing of conversational text-to-text agents available through an API endpoint. An assessment produces a structured report with a deploy-or-fix verdict, ranked vulnerabilities, and functional scenarios; passing agents receive a Giskard Label. Giskard describes a remediation process in which its team qualifies findings, discusses severity, prioritizes issues, opens workflow tickets, and reruns tests to check fixes. Enterprise evaluation options include adversarial probes, domain-specific datasets, RAG quality metrics, customizable metrics, and human review interfaces. Deployment choices include on-premise, private cloud, and SaaS. Giskard lists EU or US data residency, access controls, audit trails, identity-provider integration, encryption, and a zero-training policy, alongside GDPR, SOC 2 Type II, and HIPAA compliance. The free plan includes a local open-source library, basic vulnerability scanning and RAG evaluation, community support, and best-effort maintenance.

Google Cloud Agent Evaluation helps measure and improve agent performance, safety, and quality. Its workflow runs from defining evaluation cases through inference, behavior-trace capture, metric calculation, result analysis, and optimization. It can generate varied, multi-turn test scenarios from agent instructions and tool definitions, and intercept tool calls to supply custom behavior, mocked data, or simulated errors such as HTTP 503 responses and latency spikes. Prebuilt or custom raters score traces with reference-based Exact Match or reference-free Helpfulness metrics. Teams can also evaluate production-traffic traces or external logs without a managed test environment. Prompt optimization identifies failure points and proposes focused system-instruction changes. The service supports tool-call checks, safety evaluations, regression runs, trace ingestion, and custom models. Access is through API or web, with notebook launch options for Colab, Colab Enterprise, Agent Platform Workbench, and GitHub. Pricing is usage-based: $0.00003 per 1k input characters and $0.00009 per 1k output characters for computation-based metrics; model-based metrics also incur underlying autorater model costs. New Google Cloud customers get $300 in trial credits.