Agent Platform Eval Flywheel

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](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.

1. Tier M: Read-only with Compute Costs (client.evals.run_inference, client.evals.evaluate, client.evals.generate_user_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. Once granted once, you do not have to prompt for future evaluation.

Setup

Install the SDK:

pip install google-cloud-aiplatform[evaluation]>=1.154.0 google-genai>=1.0.0

Need GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_LOCATION. Check env vars first; if missing, ask the user. Newer Gemini models often need location="global".

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 so it passes." | Hides real failures. Fix the agent, 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 dataset, not the agent." | If expected outputs keep moving, the agent has a behavior problem. | | "I can tell from the trace it works — skip Stage 3." | Self-grading doesn't generalize. 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):

``python from agentplatform import types dataset = types.EvaluationDataset(eval_cases=[ types.EvalCase(prompt="What is 2+2?", response="4", reference="4"), # For multi-turn agent traces, set agent_data instead of prompt/response. ]) ``

Multi-turn agent traces wrap each conversation in AgentData → ConversationTurn → AgentEvent. See [references/datasetschema.md](references/datasetschema.md) for the full type hierarchy.

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

```python 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(evaldatasetdf=df) ```

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

• Cold start (no data at all): synthesize scenarios server-side with

client.evals.generate_user_scenarios(...) and a UserScenarioGenerationConfig (user_scenario_count, simulation_instruction, environment_data). Stage 2 plays them out.

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_inference(model="gemini-2.5-flash", src=dataset)

# Synthesized scenarios — let the simulator drive.
client.evals.run_inference(
    model=agent_callable,
    src=dataset,
    user_simulator_config=UserSimulatorConfig(max_turn=10),
)

# DataFrame also works as src= — no EvalCase wrapping needed.
client.evals.run_inference(model="gemini-2.5-flash", src=df)

3. Grade (always run)

result = client.evals.evaluate(dataset=dataset, metrics=[...])

Pick metrics by what you want to measure. Full catalog in [references/metricregistry.md](references/metricregistry.md).

Agent metrics (multi-turn, adaptive rubrics) — start here for agent eval.

| Goal | Metric | | --------------------------------------------- | ------------------------------- | | Did the agent achieve the user's goal? | multi_turn_task_success | | Was the reasoning path logical and efficient? | multi_turn_trajectory_quality | | Tool/function calling quality across turns | multi_turn_tool_use_quality | | Overall conversational quality | multi_turn_general_quality | | Final response quality (no reference needed) | final_response_quality | | Final response vs. a golden reference | final_response_match | | Single-turn tool use | tool_use_quality |

General quality metrics (single-turn, adaptive rubrics) — for model eval.

| Goal | Metric | | ----------------------------------------------------- | ----------------------- | | Overall response quality (recommended starting point) | general_quality | | Linguistic quality (fluency, coherence, grammar) | text_quality | | Adherence to specific constraints / instructions | instruction_following |

Static rubric metrics (fixed criteria) — apply alongside the above.

| Goal | Metric | | ------------------------------------------------- | --------------- | | Catch hallucinated claims (RAG, factual answers) | hallucination | | Factuality / consistency against provided context | grounding | | Safety policy compliance | safety |

Domain-specific check no built-in covers: write a custom metric.

• Predefined: types.RubricMetric. — server-side AutoRater, no

judge model needed.

• Custom LLM-as-a-judge: types.LLMMetric with prompt_template or

types.MetricPromptBuilder for structured rubrics.

• Custom code: types.CodeExecutionMetric with a custom_function

string containing def evaluate(instance: dict) for remote sandboxed execution; or types.Metric with custom_function= for local execution.

Always persist the result so Stage 4 and 5 can read it. Save both JSON (machine-readable, diffable) and HTML (human-readable, linkable):

import datetime
from pathlib import Path

from agentplatform._genai import _evals_visualization

out_dir = Path("artifacts/grade_results")
out_dir.mkdir(parents=True, exist_ok=True)
ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")

result_json = result.model_dump_json()
(out_dir / f"results_{ts}.json").write_text(result_json)

html = _evals_visualization.get_evaluation_html(result_json)
(out_dir / f"results_{ts}.html").write_text(str(html))

Or after the fact: scripts/render_html_report.py --type evaluation or scripts/inspect_results.py --save-html.

4. Analyze Failures

Read summary_metrics and eval_case_results — never fabricate scores. Use scripts/inspect_results.py --failing-only to filter to failures.

For each failed metric, see [references/failurepatterns.md](references/failurepatterns.md) for deeper diagnoses. The compact mapping:

| Failing metric | What to change | | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------------- | | multi_turn_task_success low | The agent isn't completing the goal — fix orchestration, missing tool calls, premature termination, wrong tool selection. | | multi_turn_trajectory_quality low | The agent reaches the goal inefficiently — refine planning prompts, remove redundant tool calls. | | multi_turn_tool_use_quality low | Fix tool descriptions, parameter docstrings, or agent instructions for tool selection. | | final_response_quality low | Read auto-generated rubric verdicts; refine instructions to address the worst-scoring criterion. | | final_response_match low | The agent's final answer doesn't match the golden reference — adjust response format or update the reference. | | hallucination low | Tighten instructions to stay grounded in tool output; verify the tool actually returned the claimed data. | | grounding low | The response contradicts the provided context — add explicit "cite only from context" instructions. | | safety low | Add safety guardrails; review the violating content category in the rubric verdict. | | general_quality / text_quality low | Adjust system instruction wording; the model's default phrasing is too generic for the task. | | instruction_following low | The agent is ignoring constraints — restate them in the system instruction or use stricter wording. | | Agent calls wrong tools | Fix tool descriptions, agent instructions, or tool_config. | | Agent calls extra tools | Add explicit stop instructions, or switch to multi_turn_tool_use_quality to surface the extra calls in the rubric. |

For 10+ failures on the same metric, use the Error Analysis service to cluster failures into themes (L1/L2 taxonomy categories) instead of reading every trace:

# Only supports multi_turn_task_success and multi_turn_tool_use_quality.
# Service runs in the global region.
analysis_client = agentplatform.Client(project="PROJECT_ID", location="global")
response = analysis_client.evals.generate_loss_clusters(
    eval_result=result,
    metric="multi_turn_task_success",
    config={"max_top_cluster_count": 5},
)
for r in response.results:
    for cluster in r.clusters:
        print(
            f"[{cluster.taxonomy_entry.l1_category}/"
            f"{cluster.taxonomy_entry.l2_category}] "
            f"{cluster.item_count} cases — {cluster.taxonomy_entry.description}"
        )

Save response.model_dump_json() and render with scripts/render_html_report.py --type loss-analysis.

5. Optimize & Iterate

Apply a fix targeting the failing metric. Re-run Stage 3. Compare with scripts/compare_results.py --baseline --candidate to confirm the target improved AND no other metric regressed.

Track progress across iterations:

| Iteration | Metric A | Metric B | Change made | | --------- | -------- | -------- | ----------------------- | | Baseline | 0.62 | 0.55 | — | | v2 | 0.78 | 0.68 | Added grounding prompt | | v3 | 0.81 | 0.72 | Fixed tool selection |

Expect 5–10+ iterations per failing case. Only after a case passes should you expand coverage with more eval cases.

Proving your work

Never claim eval results you didn't read from an actual result object.

• After running eval, print the summary_metrics table

(scripts/inspect_results.py).

• After a fix, show before/after via scripts/compare_results.py.
• Before declaring success, confirm ALL cases pass — not just the one you

were working on.

If you can't produce the evidence (SDK call failed, result truncated, metric unsupported), say so explicitly. Don't paper over gaps.

Rules of Engagement

1. Always Plan First: Before writing a script, output a `` block

detailing the steps you are about to take.

1. Step-by-Step Execution: Write the script, execute it, wait for

output, then analyze. Don't do everything in one response.

1. Standard Python: Use standard Python imports (`import

agentplatform, from google.genai import types`). Don't use internal import paths.

1. Verify Before Guessing: When unsure about SDK types or metrics,

check the SDK source code rather than guessing or hallucinating.

SDK Quick Reference

import agentplatform
from agentplatform import types
from google.genai import types as genai_types
import pandas as pd

# Initialize client
client = agentplatform.Client(project="PROJECT_ID", location="LOCATION")

# --- SINGLE-TURN EVAL (EvalCase list) ---
dataset = types.EvaluationDataset(eval_cases=[
    types.EvalCase(prompt="Query here", response="Model response here"),
])

# --- SINGLE-TURN EVAL (pandas DataFrame) ---
df = pd.DataFrame({
    "prompt":   ["Q1", "Q2"],
    "response": ["A1", "A2"],
})
dataset = types.EvaluationDataset(eval_dataset_df=df)

# --- MULTI-TURN AGENT EVAL ---
agent_data = types.evals.AgentData(
    agents={"my_agent": types.evals.AgentConfig(
        agent_id="my_agent", instruction="You are helpful.")},
    turns=[types.evals.ConversationTurn(turn_index=0, events=[
        types.evals.AgentEvent(author="user",
            content=genai_types.Content(role="user",
                parts=[genai_types.Part(text="Hello")])),
        types.evals.AgentEvent(author="my_agent",
            content=genai_types.Content(role="model",
                parts=[genai_types.Part(text="Hi! How can I help?")])),
    ])],
)
dataset = types.EvaluationDataset(
    eval_cases=[types.EvalCase(agent_data=agent_data)])

# --- METRICS ---
predefined = types.RubricMetric.MULTI_TURN_TRAJECTORY_QUALITY
custom_llm = types.LLMMetric(name="tone",
    prompt_template="Is this polite? Response: {response}")
custom_code = types.CodeExecutionMetric(name="check",
    custom_function='def evaluate(instance): return {"score": 1.0}')

# --- EVALUATE ---
result =

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [google](https://github.com/google)
- **Source:** [google/skills](https://github.com/google/skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.