evaluation

You can build agent evaluation systems by running provided scripts to execute deterministic checks, apply multi-dimensional rubrics, and perform LLM-based evaluation to measure performance and validate context engineering systematically.

Multi-dimensional rubrics for agent evaluation capture quality dimensions like factual accuracy, completeness, and tool efficiency to systematically assess agent performance, validate context engineering, and catch regressions.

To catch regressions in agent pipelines, integrate continuous evaluation into your development workflow using regression suites and quality gates to systematically monitor for performance drops and validate context engineering.

Yes, continuous monitoring of agent performance in production systems tracks key performance drivers like token usage, tool calls, and model choice to measure outcomes and catch regressions continuously.

Comparing agent configurations and measuring improvements requires systematic evaluation using deterministic checks and multi-dimensional rubrics to quantify performance differences across agent pipelines and validate context engineering.

Setting up quality gates for agent pipelines requires no external dependencies, utilizing built-in scripts and references to establish deterministic checks, regression suites, and continuous monitoring for performance measurement.