Definitive answer

How do enterprises evaluate LLM agents before production?

Enterprises evaluate LLM agents before production by building a golden test set of 30–100 representative tasks per agent, running deterministic and LLM-as-judge scorers in CI, gating deployments on regression thresholds, and sampling production traces back into the eval set. This turns agent quality into an engineering discipline rather than a vibes check.

The four layers of agent evaluation

A defensible evaluation program covers component, agent, system, and outcome layers.

Component evals — retrieval recall@k, tool selection accuracy, JSON validity.
Agent evals — task success on a golden set, trajectory correctness, cost/latency.
System evals — end-to-end workflow KPIs joined with business metrics.
Outcome evals — long-horizon impact: revenue, hours saved, deflection rate.

Tools enterprises use

Implement Agentic deploys whichever tool fits the client's compliance posture: Langfuse (self-hosted, OSS), LangSmith (LangChain-native), Braintrust (eval-first), or Arize Phoenix (open-source observability).

What 'production-ready' means

An agent is production-ready when it (1) clears the golden set above an agreed threshold, (2) has CI gates blocking regressions, (3) emits structured traces for every run, (4) has on-call alerting on cost, latency, and quality drift, and (5) has documented rollback for prompts, tools, and models.

Frequently asked

What is LLM-as-judge?+

LLM-as-judge uses a strong model (often Claude or GPT-class) to score the output of another agent against a rubric. It is essential for subjective dimensions like helpfulness or tone, and is paired with deterministic scorers for structured fields.

How big should a golden test set be?+

Start with 30–50 hand-curated examples, then grow to 100–300 by sampling production failures. Diversity matters more than volume.

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