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name: agent-platform-eval-flywheel metadata: version: "1.0.2" category: AiAndMachineLearning description: >- Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

Agent Platform Eval Flywheel Skill

Help users evaluate and iteratively improve GenAI models and agents using the Agent Platform GenAI Evaluation SDK (google.genai / agentplatform).

When to use this skill

• Evaluating GenAI agents or models with the Agent Platform GenAI Evaluation SDK (client.evals.evaluate()).
• Creating evaluation datasets from session traces, pandas DataFrames, or synthetic generation.
• Selecting, configuring, or writing custom evaluation metrics.
• Analyzing rubric verdicts, loss patterns, and clustering failures.
• Suggesting concrete code/prompt improvements based on eval results.
• Evaluating a model served on an Agent Platform endpoint (BYOM) or a Model-as-a-Service (MaaS) model by ID — including deploying the model first if needed. For this case, follow references/deployment.md and use the endpoint_evaluation.py / maas_evaluation.py scripts.

Safety & Confirmation Tiers (CRITICAL)

Before executing any commands or scripts on behalf of the user, you MUST adhere to the following safety tiers based on the action requested:

1. Tier R: Read-only (inspect_results.py, compare_results.py, validate_dataset.py, parse_adk_traces.py, render_html_report.py)
   • Rule: No confirmation needed. You may execute these helper scripts immediately to inspect data, validate schemas, parse traces, or compare evaluation results.
2. Tier M: Read-only with Compute Costs (client.evals.run_inference, client.evals.evaluate, client.evals.generate_conversation_scenarios, client.evals.generate_loss_clusters)
   • Rule: These operations invoke LLMs or remote evaluation services that consume compute resources and incur costs. This requires interactive confirmation with 'Yes'/'No' options.
   • Confirmation for EVERY evaluation run: Every evaluation, re-evaluation, metric update, parameter change, or synthetic scenario generation requires its own dry-run preview and interactive confirmation. Never execute a second evaluation, comparison pass, or modified evaluation without presenting a new confirmation preview and obtaining user approval.
   • Same-turn restriction: Do not run the evaluation in the same turn as presenting the confirmation prompt. End your turn after asking and wait for the user's reply; only execute after explicit 'Yes' / approval. Printing a preview and then calling the tool before the user can answer does not count as obtaining confirmation.
   • No Pre-Execution of Remote Evaluation: NEVER execute client.evals.evaluate(), client.evals.run_inference(), client.evals.generate_conversation_scenarios(), or run any script invoking these remote operations before user confirmation. In the initial turn, you may prepare local data structures and compose the script, but you MUST present the dry-run preview card and obtain explicit user confirmation before running any remote evaluation or scenario generation call.
   • Immediate Execution Upon Approval: Once the user explicitly approves (e.g., 'Yes', 'Approved', 'Go ahead', 'Proceed'), proceed directly to executing the previewed evaluation script via run_command and report the results. Do not conclude the turn without executing the approved action.

Setup

The scripts need vertexai (from google-cloud-aiplatform[evaluation]), google-genai, pandas, and requests. Do not create a virtual environment — it starts empty and hides packages the environment already provides, forcing a redundant install. Probe, and install only what is missing:

python3 -c "import vertexai, google.genai, pandas, requests" \
  || pip install 'google-cloud-aiplatform[evaluation]>=1.163.0' 'google-genai>=1.0.0'

The version specifiers must stay quoted: unquoted, bash reads >=1.154.0 as a redirect and silently writes an empty file instead of constraining the install.

Need GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION.

• Preserve User Project and Location: Always prioritize the user's explicitly provided project and location (e.g. project='<PROJECT_NUMBER>', location='us-central1'). Never change or override the user's requested location to 'global' unless the user explicitly requested 'global'.
• Missing Parameters: If the user's request omits the project or location, you MUST pause in your response and ask the user for the missing location/project before preparing or running the evaluation.

Correct SDK entrypoints

import agentplatform
client = agentplatform.Client(project=PROJECT, location=LOCATION)

client.evals.run_inference(model=..., src=...)
client.evals.evaluate(dataset=..., metrics=...)
client.evals.generate_conversation_scenarios(...)

Two imports that look plausible and are not:

• from agentplatform.types import evals -- ModuleNotFoundError. types is a module, not a package; use from agentplatform import types.
• from vertexai.evaluation import PointwiseMetric, EvalTask -- the superseded SDK. Its classes take different arguments (PointwiseMetric has no system_instruction), so code written against it fails with TypeError rather than an import error. Use agentplatform throughout.

The Quality Flywheel

Five stages, run in order on the first pass, then loop 2 → 5 until quality targets are met.

Shortcuts that waste time

| Shortcut | Why it fails | | ------------------------------------ | ------------------------------------ | | "I'll tune the metric threshold down | Hides real failures. Fix the agent, | : so it passes." : not the bar. : | "This case is flaky, I'll skip it." | Flakiness reveals non-determinism in | : : the agent. Fix with temperature=0 : : : or stricter instructions. : | "I just need to fix the eval | If expected outputs keep moving, the | : dataset, not the agent." : agent has a behavior problem. : | "I can tell from the trace it works | Self-grading doesn't generalize. | : — skip Stage 3." : Always run evaluate() and read : : : scores. : | "One iteration is enough." | Expect 5–10+ iterations. Stopping | : : early leaves regressions on other : : : metrics undetected. :

1. Prepare Data

Produce an EvaluationDataset. There are three input shapes, pick the one that matches the data the user already has:

• EvalCase list (single-turn or multi-turn):

  from agentplatform import types
  from google.genai import types as genai_types
  
  # prompt/reference/response values are Content, not str. UserContent and
  # ModelContent wrap a plain string and set the right role.
  dataset = types.EvaluationDataset(eval_cases=[
      types.EvalCase(
          prompt=genai_types.UserContent("What is 2+2?"),
          responses=[types.ResponseCandidate(
              response=genai_types.ModelContent("4"))],
          reference=types.ResponseCandidate(
              response=genai_types.ModelContent("4")),
      ),
      # For multi-turn agent traces, set agent_data instead of prompt/responses.
  ])
  

  Multi-turn agent traces wrap each conversation in AgentData → ConversationTurn → AgentEvent. See references/dataset_schema.md for the full type hierarchy.

• Pandas DataFrame (tabular sources — CSV, BigQuery, Sheets):

  import pandas as pd
  from agentplatform import types
  
  df = pd.DataFrame({
      "prompt":    ["What is 2+2?", "Capital of France?"],
      "response":  ["4",            "Paris"],
      "reference": ["4",            "Paris"],
  })
  dataset = types.EvaluationDataset(eval_dataset_df=df)
  

  Column names must match the fields the chosen metrics expect (see references/dataset_schema.md for the per-metric requirements table).

• Cold start (no data at all): synthesize scenarios server-side with client.evals.generate_conversation_scenarios(agent=..., config=...) -- the parameter is agent or agent_info, not agents, and config is required. The config class is types.evals.UserScenarioGenerationConfig, not types.UserScenarioGenerationConfig. Set its user_scenario_count (1-100): it defaults to None, the client accepts that, and the server rejects the call with 400 INVALID_ARGUMENT. count is a separate field and does not substitute for it. Stage 2 plays the scenarios out.

  • CRITICAL - Underspecified Requests: When asked to synthesize scenarios, if the request omits required parameters (such as location, environment_data, simulation_instruction, or model_name), do NOT assume defaults or guess values. You MUST pause in your first turn and explicitly ask the user for the missing information (e.g., "Please provide the missing simulation instructions, environment data, model name, and location"). Only proceed with the dry-run preview after the user provides them.
  • Friction & Parameter Changes: When asked to generate synthetic user scenarios, if the user modifies requested parameters (such as scenario count, model, or instructions) or pushes back, you MUST present a revised dry-run confirmation card with the updated parameters and wait for explicit user approval before executing generation code via run_command. Do NOT generate scenarios directly in plain text.

• Managed Agents (Gemini Agents API): evaluate agents created with the Managed Agents API. Use generate_conversation_scenarios to create test scenarios from the agent's configuration, run_inference to execute the agent, and evaluate to score the traces. These functions now accept managed agents and interaction ids as input. You can also evaluate existing interactions recorded via the Interactions API using InteractionsDataSource. See references/sdk_patterns.md Pattern 8 for the full code pattern.

For ADK session dumps, use scripts/parse_adk_traces.py instead of writing the conversion by hand.

2. Run Inference

Populate responses/traces on the dataset. Skip this stage if traces are already complete (e.g., production logs or replay).

# Agent eval — pass a callable wrapping the user's ADK Agent/App.
client.evals.run_inference(model=agent_callable, src=dataset)

# Model eval — pass a model ID directly.
client.evals.run_inferen