Evaluation and Observability

Evaluation frameworks provide standardized tooling for defining test cases, running them against agent systems, and comparing results across different configurations of prompts, models, and tools. Key options include Promptfoo (an open-source CLI tool for comparing prompt variations), Braintrust (an end-to-end eval platform with trace analysis), and LangSmith (eval and observability integrated into the LangChain ecosystem), each handling the infrastructure that makes eval-driven development practical: test case management, parallel execution, regression detection, and human review for ambiguous outputs.

You build deterministic evaluations first because they are the cheapest and fastest to run: fixed, rule-based criteria with binary pass/fail outcomes (does the output match the expected regex? does the code compile? do all required fields appear in the JSON?) give you an immediate feedback loop that costs fractions of a cent per run. Probabilistic evaluations use statistical methods or a language model acting as a judge to assess quality on a spectrum, capturing nuanced dimensions like helpfulness or reasoning quality that resist reduction to a rule, but they introduce variance that requires larger sample sizes to trust.

You cannot improve what you cannot measure, and most agent quality is not obviously measurable: task completion looks binary but collapses when you ask whether the task was completed correctly, efficiently, and safely at the same time. The metrics teams actually track in production are task completion rate, correctness, token and tool-call efficiency, latency, and error rate; the metrics they aspire to track, like code quality or architectural appropriateness, resist automated measurement and require a large language model acting as a judge or periodic human review.