---

id: PRD-010 title: Evaluation Framework status: DRAFT domain: evaluation depends_on: [PRD-001, PRD-004, PRD-005] key_decisions: [llm-as-judge-cross-family, golden-dataset-structure, ci-eval-gate, eval-staging-isolation]

PRD-010 — Evaluation Framework

Field	Value
Document ID	PRD-010
Version	1.0
Status	DRAFT
Date	March 2026
Parent Doc	PRD-001
Related Docs	PRD-004 (Agent Layer), PRD-005 (LangSmith Observability)

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Overview

This document specifies the evaluation framework for AgentOps Dashboard: how agent output quality is measured, what data is used as ground truth, how scores are computed, and how evaluations are gated in CI.

The framework is built on LangSmith's evaluation primitives (datasets, evaluators, experiments) and runs automatically on every prompt change and deployment.

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Evaluation Dimensions

What Gets Evaluated

The eval framework measures three things:

Dimension	Question	Evaluator Type
Triage Accuracy	Does the root cause match what a human engineer would identify?	LLM-as-judge + human comparison
Report Usefulness	Is the final report helpful and actionable?	LLM-as-judge (rubric)
Question Quality	When the supervisor asks the user a question, is it a good question?	Human feedback (thumbs up/down in UI)
Agent Efficiency	Did the supervisor route optimally (no redundant agent calls)?	Automated: count supervisor hops vs. minimum path

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LLM-as-Judge Setup

from typing import Literal
from pydantic_settings import BaseSettings
from langchain_anthropic import ChatAnthropic
from langsmith.evaluation import evaluate, LangChainStringEvaluator


class EvalSettings(BaseSettings):
    langchain_project: Literal["agentops-staging"]  # fails fast if pointed at production
    langsmith_api_key: str                           # required to submit eval results to LangSmith
    langserve_base_url: str                          # must be a staging deployment URL
    openai_api_key: str                              # separate eval project key for billing isolation



def main() -> None:
    """Run the LangSmith evaluation suite against the golden dataset."""
    settings = EvalSettings()  # raises ValidationError if env vars are missing or wrong

    helpfulness_evaluator = LangChainStringEvaluator(
        "criteria",
        config={
            "criteria": {
                "helpfulness": "Is this triage report specific, actionable, and correct?",
                "completeness": "Does the report cover severity, root cause, relevant files, and a fix suggestion?",
                "accuracy": "Does the root cause match the reference answer?"
            },
            "llm": ChatAnthropic(model="claude-sonnet-4-6", temperature=0)
        }
    )

    evaluate(
        lambda inputs: run_triage_job(inputs["issue_url"]),
        data="agentops-golden-dataset-v1",
        evaluators=[helpfulness_evaluator],
        experiment_prefix="prompt-change-2026-03",
    )


if __name__ == "__main__":
    main()

The judge uses a different model family (Anthropic) than the production agents (OpenAI GPT-4o) to avoid self-preference bias, which research shows inflates scores by 10–25% when a model evaluates its own outputs.

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Scoring Rubric

Score	Meaning
5	Perfect: root cause is correct, files are exact, report is clear and actionable
4	Good: root cause is correct, minor gaps in files or report formatting
3	Partial: hypothesis is on the right track but root cause is incomplete or imprecise
2	Poor: wrong code area identified, or report is too vague to be actionable
1	Fail: completely wrong diagnosis or empty output

Target: Average score ≥ 4.0 / 5.0 on the golden dataset before any prompt change is deployed to production.

---

Golden Dataset

Structure

The golden dataset is a collection of real GitHub issues with human-authored reference answers:

{
    "issue_url": "https://github.com/org/repo/issues/1042",
    "issue_title": "Auth token expiry causes 500 on /api/me",
    "issue_body": "...",
    "reference": {
        "severity": "HIGH",
        "root_cause": "JWT expiry check in auth/middleware.py:L142 uses local time instead of UTC",
        "relevant_files": ["auth/middleware.py", "tests/test_auth.py"],
        "expected_keywords": ["JWT", "UTC", "timezone", "token expiry"]
    }
}

Dataset Growth Plan

Phase	Dataset Size	Source
v1.0 launch	20 issues	Manually authored from real repos
v1.1	50 issues	User feedback thumbs up/down on job outputs
v2.0	200+ issues	Crowdsourced from community contributors

Dataset Management

The golden dataset is managed in LangSmith's Datasets UI. New examples can be added directly from a LangSmith trace: if a live production job produces a high-quality output, it can be added to the dataset in one click via LangSmith's "Add to Dataset" feature.

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Automated Eval Pipeline

When Evals Run

Trigger	Action
Any agent prompt change proposed in a PR to main	CI pipeline runs eval against golden dataset; fails PR if avg score drops > 0.3
New LangServe agent version deployed to staging	Eval runs automatically; results posted to PR as a comment
Daily at 02:00 UTC	Production eval: random sample of 10 recent jobs scored and logged
Manual trigger	Developer can run evals on demand from LangSmith UI or CLI

CI Integration

# .github/workflows/eval.yml
- name: Run LangSmith Evals
  run: |
    python scripts/run_evals.py \
      --dataset agentops-golden-dataset-v1 \
      --project agentops-staging \
      --min-score 4.0 \
      --fail-on-regression

Eval Environment Requirements

CI-triggered evals (PR gate and staging-deploy trigger) call run_triage_job against the golden dataset. They must run in a fully isolated environment:

Resource	Production value	Eval (CI) value	Why
LangSmith project	agentops-prod	agentops-staging	Prevent eval traces from polluting production dashboards
LangServe URL	https://agents.prod/…	https://agents.staging/…	Prevent eval traffic from consuming production rate-limit budget
OpenAI API key	Production org/project	Separate org sub-account or project	Billing isolation; eval cost tracked separately from user traffic

Staging deployment requirement: A dedicated staging deployment of all LangServe agents (investigator, codebase-search, web-search, critic, writer) must be maintained and kept in sync with main. CI evals fail if the staging deployment is unreachable.

Daily production eval (02:00 UTC) is exempt from the above: it scores completed job outputs already stored in LangSmith and does not call run_triage_job on new inputs. Production credentials are appropriate for that trigger.

CI env vars (set in GitHub Actions secrets):

{% raw %}

LANGCHAIN_PROJECT: agentops-staging
LANGSMITH_API_KEY: ${{ secrets.LANGSMITH_STAGING_KEY }}
LANGSERVE_BASE_URL: ${{ secrets.LANGSERVE_STAGING_URL }}
OPENAI_API_KEY: ${{ secrets.OPENAI_EVAL_KEY }}

{% endraw %}