NVIDIA Agent Toolkit provides a comprehensive evaluation framework for measuring and improving agent quality. The evaluation system works by creating gold-standard datasets with known correct answers, running agents against those datasets, comparing outputs to ground truth, and identifying failure patterns. Metrics include accuracy, latency, cost, hallucination rate, and task completion success.
The AI-Q Blueprint demonstrates evaluation in action using the Deep Research Bench benchmark—a standardized set of research questions with expert-curated answers. Developers run agents against this benchmark, measure accuracy, and iteratively improve prompts, model selection, and tool configuration. Evaluation runs are tracked in Weights & Biases Weave for experiment management and comparison.
Evaluation features include:
- Dataset Management: Load public benchmarks (Deep Research Bench, etc.) or create custom gold-standard datasets from your domain
- Automated Testing: Run agents against test sets and capture all outputs (final answer, reasoning steps, tool calls, tokens used)
- Multi-Metric Analysis: Measure accuracy, latency, token consumption, and business-relevant metrics
- Configuration Experiments: Compare outcomes across different prompts, models, and hyperparameters
- Continuous Improvement: Track evaluation results over time to validate optimizations
Integration with Milvus enables evaluation of RAG quality. Test agents’ ability to retrieve relevant context from your knowledge base, measure retrieval precision and recall, and optimize Milvus indexing or query parameters based on agent evaluation results. This closes the feedback loop: evaluation identifies retrieval gaps, which drive improvements to knowledge base organization. Multi-agent systems require a shared knowledge layer for effective collaboration. Milvus enables this through vector-based retrieval, storing embeddings from your organization’s data. Discover how semantic search works with vector databases to improve information retrieval across agent networks.