Model Evaluation Features Now Available in Gemini Enterprise Agent Platform
Enhanced tools for monitoring and improving AI agent performance now generally available.
1Agent Quality Monitoring
Gemini Enterprise Agent Platform enables consistent measurement of AI agents during development and production phases. It uses the same quality scoring system for local experiments and live traffic, simplifying issue detection in production. Users can evaluate agents through the Agent Platform SDK, agents-cli, or Google Cloud console, ensuring comprehensive performance analysis across all stages of agent deployment.
2Flexible Evaluation Interface
The platform's evaluation service provides a comprehensive UI for defining metrics, initiating new runs, and reviewing results down to individual failures. Users can examine complete session logs for detailed troubleshooting. All experiment artifacts are stored transparently in Cloud Storage, offering version control and audit capabilities essential for enterprise compliance and historical tracking of agent performance improvements.
3Local and Live Evaluation Options
Developers can run local experiments for rapid iterations or evaluate deployed agents with telemetry enabled. The user simulator feature creates traces before real user interactions occur. For large-scale evaluations, the platform's issue clustering organizes failures into actionable groups based on custom taxonomies, significantly reducing debugging time for complex agent behaviors in production environments.
4Pre-built Metrics and Adaptive Rubrics
The service includes over 20 pre-built metrics like ROUGE for summarization and BLEU for translation. The adaptive rubric system, co-developed with Google DeepMind, generates case-specific pass/fail tests evaluating various aspects including task success, tool use quality, and safety compliance. These rubrics provide detailed verdicts with rationales, enabling precise performance tuning of AI agents across different operational dimensions.
5Custom Metrics and Model Support
When pre-built metrics don't fit specific needs, users can create Python-based custom metrics for exact text matches or JSON validation. The LLM-as-a-judge feature supports custom criteria and rating scales. The platform accommodates models from any provider for local runs and all Model Garden models (including Gemini and Anthropic) for server-side evaluations, offering unparalleled flexibility in assessment methodologies.
6Production Performance Monitoring
The platform's online monitors track post-deployment agent performance using data collected via Cloud Trace. Cost-effective sampling and targeted filtering options make continuous monitoring feasible. Built-in dashboards visualize performance trends over time, while drift alerts can be configured for various channels including email and Slack, ensuring teams promptly address emerging performance issues in production environments.
7Automated Test Case Generation
The platform's case generator creates synthetic test scenarios from agent instructions, eliminating blank-page syndrome. The user simulator enables multi-turn conversation testing by emulating defined personas. The environment simulator intercepts agent calls to backend systems, allowing testing of failure scenarios without impacting production, significantly expanding test coverage beyond manually scripted cases.
8Enterprise-Ready Solution
With general availability, Gemini Enterprise Agent Platform's evaluation features now offer enterprise-grade security and regional support. Google continues expanding support for specialized tasks and input modalities while actively seeking user feedback. This comprehensive solution addresses critical needs in AI agent reliability, establishing a new standard for performance monitoring in conversational AI systems across industries.