Google Agents Cli Eval

Agent Evaluation Guide

Requires: agents-cli (uv tool install google-agents-cli) — install uv first if needed.

Scaffolded project? If you used /google-agents-cli-scaffold, you already have agents-cli eval run (chains generate + grade), tests/eval/datasets/, and tests/eval/eval_config.yaml. Start with executing eval run and iterate from there.

Reference Files

File	Contents
references/dataset_schema.md	Canonical EvaluationDataset schema — all field types, JSON examples for single-turn / multi-turn / multi-agent, common mistakes
references/metrics-guide.md	Complete metrics reference — all built-in metrics, match types, custom metrics, judge model config
references/user-simulation.md	Dynamic conversation testing — eval dataset synthesize flags, what scenarios are, compatible metrics
references/builtin-tools-eval.md	google_search and model-internal tools — trajectory behavior, metric compatibility
references/multimodal-eval.md	Multimodal inputs — eval dataset schema, built-in metric limitations, custom evaluator pattern

---

The Quality Flywheel

Improving agent quality is iterative. The 5 stages below describe the loop. Each stage has a Default path (you, the coding agent, do the work directly) and an Opt-in CLI command that delegates to the Agent Platform Eval Service for better quality and scale.

1. Prepare Data

Default: Use or edit the scaffolded tests/eval/datasets/basic-dataset.json to define single-turn eval inputs. Start with 1–2 cases.

Opt-in: agents-cli eval dataset synthesize — user-simulate multi-turn datasets when you lack data; its output includes traces, so skip Stage 2 and grade directly. See Eval Commands and references/user-simulation.md.

2. Run Inference

agents-cli eval generate — executes the agent over the dataset and writes traces to artifacts/traces/. Run this when you wrote the dataset by hand in Stage 1 (default path). Skip this stage if you used eval dataset synthesize — that command already produced traces.

3. Grade Traces (always run)

agents-cli eval grade — scores the traces and writes results_<ts>.{json,html} to artifacts/grade_results/. No opt-in alternative; this is the core. Always run, regardless of how Stages 1 and 2 produced the traces.

Shortcut: agents-cli eval run chains Stages 2 + 3 in one command using the default artifacts/traces/ directory between them. Use it for the common path; drop back to the two-step form when you need a custom traces location or want to grade an existing traces file.

4. Analyze Failures

Default: Open the latest artifacts/grade_results/results_<ts>.html (or .json) and identify failed metrics — see What to fix when scores fail below for the fix table.

Opt-in: agents-cli eval analyze — LLM-based failure clustering; prefer when you have 10+ failing cases and want categorized failure modes. See Eval Commands.

5. Optimize & Code Fix

Default: Edit the agent — adjust prompts, tool descriptions, instructions, or eval dataset based on the failure analysis. See What to fix when scores fail below for the failure → fix mapping.

Opt-in: agents-cli eval optimize — runs ADK GEPA prompt optimization against a target metric. Suitable for prompt-only failures. The optimized prompt appears in the command output; capture it and apply it to the agent. For the full per-iteration trace, set print_detailed_results: true in your optimization config file.

Long-running and expensive. GEPA optimization makes many LLM calls and can take a long time. Do not run it unless the user explicitly asks for prompt optimization. When you do run it, iterate as far as possible with manual fixes first, then run a single final eval optimize — never loop on this command.

Running the loop

Iterate stages 2 → 3 → 4 → 5 → 2 (or 1 → 3 → 4 → 5 → 1 if using synthesize). After each fix, run agents-cli eval compare <prev_results>.json <new_results>.json to confirm the target metric improved without regressing others. Expect 5–10+ iterations per case before it passes — this is normal. Only after a case passes should you expand coverage with more eval cases.

When doing 5+ iterations, maintain a task list of which cases are fixed, which are still failing, and what fixes you've tried. Prevents re-attempting the same fix.

Shortcuts That Waste Time

Recognize these rationalizations and push back — they always cost more time than they save:

Shortcut	Why it fails
"I'll tune the eval thresholds down to make it pass"	Lowering thresholds hides real failures. If the agent can't meet the bar, fix the agent — don't move the bar.
"This eval case is flaky, I'll skip it"	Flaky evals reveal non-determinism in your agent. Fix with temperature=0, rubric-based metrics, or more specific instructions — don't delete the signal.
"I just need to fix the eval dataset, not the agent"	If you're always adjusting expected outputs, your agent has a behavior problem. Fix the instructions or tool logic first.

Choosing the Right Metrics

Pick built-in metrics by what you want to measure. Multi-turn metrics evaluate the full conversation; single-turn metrics evaluate one prompt-response pair (with intermediate tool calls). When no built-in fits, write a custom metric (see Evaluation Configuration Schema below).

Goal	Recommended built-in metrics
Did the agent achieve the user's goal? (catch-all for multi-turn agents)	multi_turn_task_success
Was the agent's reasoning path logical and efficient?	multi_turn_trajectory_quality
Quality of tool / function calling across turns	multi_turn_tool_use_quality
Final response quality (no ground-truth reference needed)	final_response_quality
Factual grounding (catch hallucinated claims, e.g., RAG agents)	hallucination
Safety policy compliance	safety
Domain-specific check no built-in covers	Write a custom LLMMetric (LLM-judge) or CodeExecutionMetric (deterministic Python). See Evaluation Configuration Schema below.

Run agents-cli eval metric list to see all available built-ins. For full metric definitions and rubric details, see the Agent Platform metric docs and references/metrics-guide.md.

---

What to fix when scores fail

After agents-cli eval grade completes, inspect the latest artifacts/grade_results/results_<timestamp>.json (or open the .html file) for per-case scores and judge rationales — that's the input to every fix decision below.

Failure	What to change
multi_turn_task_success low	The agent isn't completing the user's goal — fix orchestration, missing tool calls, premature termination, or wrong tool selection
multi_turn_trajectory_quality low	The agent reaches the goal inefficiently or takes wrong steps — refine planning prompts, tighten instruction order, or 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 the auto-generated rubric verdicts; refine agent instructions to address the worst-scoring criterion (often clarity, completeness, or instruction-following)
hallucination low	Tighten agent instructions to stay grounded in tool output; verify the tool actually returned the data the agent claimed
safety low	Add safety guardrails to instructions; review the violating content category in the rubric verdict
Agent calls wrong tools	Fix tool descriptions, agent instructions, or tool_config
Agent calls extra tools	Add strict stop instructions, or switch to multi_turn_tool_use_quality

After applying a fix, rerun agents-cli eval generate && agents-cli eval grade and use agents-cli eval compare <prev_results>.json <new_results>.json to confirm the fix improved the target metric without regressing others.

---

Eval Commands

All agents-cli eval subcommands support --help for the authoritative flag list and defaults — run agents-cli eval <subcommand> --help (or agents-cli eval dataset <subcommand> --help) when in doubt. The examples below show the most common invocations; flags can change between releases.

eval generate

Runs an agent over an evaluation dataset and writes traces to disk.

By default, runs the agent in a local HTTP server (launches the project's fast_api_app.py if it exists, or falls back to adk api_server) and sends each evaluation case over HTTP. You can generate traces from an already-running agent by passing its HTTP endpoint and app name to --url and --app-name.

# Basic — uses tests/eval/datasets/, writes to artifacts/traces/
agents-cli eval generate

# Advanced — custom dataset and output dir
agents-cli eval generate --dataset tests/eval/datasets/custom.json -o ./custom_traces/

# Against a deployed agent (or one you started manually)
agents-cli eval generate --url https://my-agent.run.app --app-name app

eval grade

Scores generated traces against built-in or custom metrics. Writes timestamped results_<YYYYMMDD_HHMMSS>.json (consumed by eval compare) and .html (open in a browser) into the output dir, and prints a summary table to the console.

# Basic — defaults: traces from artifacts/traces/, results to artifacts/grade_results/,
# metrics from tests/eval/eval_config.yaml's metrics_to_run
agents-cli eval grade

# Advanced 1 — grade traces from a non-default location (the canonical
# pairing for `eval generate --output custom_traces/`)
agents-cli eval grade --traces custom_traces/

# Advanced 2 — pick built-in metrics, custom output dir
agents-cli eval grade --metrics tool_use_quality,safety --output ./out/

# Advanced 3 — load metrics to run from a config file (YAML or JSON) on a specified trace file.
agents-cli eval grade --traces ./artifacts/traces/trace_1.json --config tests/eval/eval_config.yaml

See Evaluation Configuration Schema below for the config file format.

eval compare

Diffs two results_*.json files produced by eval grade. Run it after a fix to confirm the target metric improved without regressing others.

agents-cli eval compare baseline.json candidate.json

eval metric list

Lists the built-in metric names usable with eval grade --metrics.

agents-cli eval metric list

eval analyze

Runs LLM-based failure clustering and root-cause analysis over a results_*.json produced by eval grade. Use when you have 10+ failing cases and want categorized failure modes instead of reading the HTML case-by-case. Supported --metric values: multi_turn_task_success, multi_turn_tool_use_quality.

# Basic — analyze a results file with default settings
agents-cli eval analyze --eval-result artifacts/grade_results/results_<ts>.json

# Advanced — restrict to a specific metric and cap loss clusters
agents-cli eval analyze \
  --eval-result artifacts/grade_results/results_<ts>.json \
  --metric multi_turn_tool_use_quality \
  --top-k 5 \
  --output artifacts/analysis_<ts>.json

eval dataset synthesize

Generates user scenarios server-side from your agent's tools and instructions, then plays each scenario against an LLM-backed user simulator. The output is a graded-ready trace file with full agent_data.turns populated — feed it directly to eval grade (skip eval generate).

# Basic — generate 3 default scenarios (up to 5 turns each) into artifacts/traces/
# (where eval grade reads from by default, so synthesize → grade works without flags)
agents-cli eval dataset synthesize

# Advanced — guide scenario generation with optional instruction and environment context
agents-cli eval dataset synthesize \
  -n 5 \
  --instruction "Customer asking about refunds" \
  --environment-context "E-commerce support" \
  --max-turns 8 \
  -o tests/eval/datasets/refund_scenarios.json

For scenario semantics, the full eval dataset synthesize flag table, and which simulator internals are not user-configurable, see references/user-simulation.md.

eval optimize

Runs ADK GEPA prompt optimization against a target metric. Suitable after eval grade identifies prompt-only failures (wording, not tool/orchestration logic). --dataset and --target-metric override values in --config when both are passed. Long-running and expensive — see Stage 5 of the Quality Flywheel for usage guidance.

# Basic — optimize against a single metric on a dataset
agents-cli eval optimize --dataset tests/eval/datasets/basic-dataset.json --target-metric final_response_quality

# Advanced — drive multi-metric / multi-dataset optimization from a config file
agents-cli eval optimize --config tests/eval/optimization_config.json

eval submit / eval results (cloud-side)

The managed, asynchronous counterpart to the local path, for large or CI-driven runs: eval submit hands the dataset and metrics to the Agent Platform Eval Service, and eval results polls and downloads the scores. Pass --resource-name <agent> to also run inference server-side (managed generate + grade); omit it to grade an existing trace (managed grade).

# Grade an existing trace server-side; returns a run resource name to poll
agents-cli eval submit --dataset tests/eval/datasets/basic-dataset.json --dest gs://my-bucket
# Add --resource-name projects/<p>/locations/<l>/reasoningEngines/<id> to run inference too

agents-cli eval results --run-id <run-resource-name>

---

Evaluation Dataset Format

An EvaluationDataset is a JSON file with an eval_cases array. Cases come in two shapes depending on how they're used:

• Inference input (what you give to eval generate) — a user prompt or a partial conversation ending in a user prompt. The agent runs and produces traces.
• Grading input (what you give to eval grade) — a complete trace including the agent's responses and tool calls. Normally produced by eval generate or eval dataset synthesize; you don't write these by hand.

See references/dataset_schema.md for the full canonical schema, all field types, and common mistakes.

Inference input format

Two shapes are supported.

(a) Simple single-turn prompt — what the scaffolded tests/eval/datasets/basic-dataset.json uses. The agent runs from scratch.

{
  "eval_cases": [
    {
      "eval_case_id": "greeting",
      "prompt": {
        "role": "user",
        "parts": [{"text": "Hello, what can you help me with?"}]
      }
    },
    {
      "eval_case_id": "weather_query",
      "prompt": {
        "role": "user",
        "parts": [{"text": "What's the weather like in San Francisco?"}]
      }
    }
  ]
}

(b) Multi-turn continuation via agent_data — a partial conversation whose last turn ends with a user message; the agent's next response is evaluated. See references/dataset_schema.md (Multi-Turn / Multi-Agent Dataset) for the JSON shape.

Grading input format (traces)

A complete trace — agent responses plus function_call / function_response parts — normally produced by eval generate / eval dataset synthesize (you don't write these by hand). Authors are "user", an agent ID from the agents map, or "tool". See references/dataset_schema.md for the trace shape, multi-agent examples, and the full type reference.

---

Evaluation Configuration Schema

agents-cli eval grade --config <path> accepts a single configuration file in either YAML (.yaml / .yml) or JSON (.json). The file declares two parts:

• metrics_to_run — the selection list of metric names to execute on this run. Names resolve to built-in metrics first, then to entries in custom_metrics.
• custom_metrics — a definition pool of custom metrics available to this project. Defining a metric here does not run it; it must also appear in metrics_to_run (or be passed via --metrics name1,name2 on the CLI, which is equivalent to overriding metrics_to_run for that invocation).

Minimal example (YAML preferred — human-readable, no JSON escaping for prompts and Python):

metrics_to_run:
  - multi_turn_task_success     # built-in
  - example_llm_metric          # selected from custom_metrics pool below
  - agent_turn_count            # selected from custom_metrics pool below

custom_metrics:
  - name: example_llm_metric
    prompt_template: |
      Rate the agent's response 1-5 for helpfulness and accuracy.
      Prompt: {prompt}
      Final response: {response}
      Full trace (for tool-call and reasoning context): {agent_data}
      Return JSON: {"score": <1|2|3|4|5>, "explanation": "<reason>"}

  - name: agent_turn_count
    custom_function: |
      def evaluate(instance):
          turns = (instance.get("agent_data") or {}).get("turns", [])
          return {'score': len(turns)}

JSON is also accepted (same field names, with prompt_template and custom_function as escaped strings) — but always prefer YAML for human-readable configs.

Each entry in custom_metrics is dispatched by field: presence of custom_function makes it a CodeExecutionMetric (deterministic Python); otherwise it's an LLMMetric (LLM-as-judge with prompt_template). Run agents-cli eval metric list to see available built-ins. For full custom-metric field reference (judge model options, sampling counts), see references/metrics-guide.md.

Agent trace field model. For datasets produced by agents-cli eval generate (or eval dataset synthesize), each eval case exposes three standard fields to a metric:

• {prompt} — the user message (or first user turn).
• {response} — the agent's final text response, extracted from the last text-bearing event. In custom_function callbacks this is instance['response'] with shape {"role": "model", "parts": [{"text": "..."}]}.
• {agent_data} — the full structured turns/events trace, useful when the judge needs to reason about tool calls or intermediate reasoning.

{reference} and {context} resolve only when the eval case has reference / context fields populated (e.g., golden-answer datasets); they are not populated by eval generate / eval dataset synthesize.

Code-based metrics default to local in-process execution (no GCP project or region required, but the evaluate(instance) function runs with the CLI's privileges). Set execution: "remote" on the metric to run it server-side in Vertex AI's CodeExecutionMetric sandbox instead — that path requires a configured GCP project + region.

---

Common Gotchas

Use Rubric-Based Tool Evaluation instead of Hardcoded Sequences

Evaluating agent tool usage using strict sequence matching is fragile because agents may call helper tools (like searches or geocoding) in different orders or perform extra proactive steps.

Instead, use multi_turn_tool_use_quality / multi_turn_trajectory_quality. These metrics automatically generate content-based and intent-based adaptive rubrics, assessing technical correctness and technical sequence logic semantically using an LLM judge rather than forcing a rigid match.

App name must match directory name

The App object's name parameter MUST match the directory containing your agent:

# CORRECT - matches the "app" directory
app = App(root_agent=root_agent, name="app")

# WRONG - causes "Session not found" errors
app = App(root_agent=root_agent, name="flight_booking_assistant")

Vertex eval region

eval grade and eval submit default to the global endpoint — they don't inherit the manifest region (the eval services support only a subset of regions), and eval analyze is global-only. Override these per run with --region <REGION> (e.g. data residency); the service rejects an unsupported one:

400 FAILED_PRECONDITION: Unsupported region for Vertex Evaluation Service: <region>

eval generate (without the --url flag) and eval dataset synthesize run your agent locally, so they honor the agent's own .env — notably GOOGLE_CLOUD_LOCATION, which selects the model endpoint when the agent uses Vertex AI (GOOGLE_GENAI_USE_VERTEXAI=true); it's unused with a GEMINI_API_KEY (AI Studio). They take no --region and never override your .env with the manifest region; change the model region by editing .env. One caveat for synthesize: its scenario-generation step is a server-side eval call at GOOGLE_CLOUD_LOCATION, so keep that an eval-supported region (global by default) even though the agent itself could run elsewhere.

No eval region fits your data-residency rules? Fall back to local custom metrics — a custom_metrics entry with a custom_function (execution: local, the default) grades in-process with no GCP region required. You lose the managed built-in metrics, but your custom_function can still call an LLM judge in a compliant region itself — so LLM-as-judge grading stays available anywhere.

The before_agent_callback Pattern (State Initialization)

Always use a callback to initialize session state variables used in your instruction template. This prevents KeyError crashes on the first turn:

async def initialize_state(callback_context: CallbackContext) -> None:
    state = callback_context.state
    if "user_preferences" not in state:
        state["user_preferences"] = {}

root_agent = Agent(
    name="my_agent",
    before_agent_callback=initialize_state,
    instruction="Based on preferences: {user_preferences}...",
)

Model thinking mode may bypass tools

Models with "thinking" enabled may skip tool calls. Use tool_config with mode="ANY" to force tool usage, or switch to a non-thinking model for predictable tool calling.

---

Common Eval Failure Causes

Symptom	Cause	Fix
Agent mentions data not in tool output	Hallucination	Tighten agent instructions; add hallucination metric
"Session not found" error	App name mismatch	Ensure App name matches directory name
Score fluctuates between runs	Non-deterministic model	Set temperature=0 or use rubric-based eval with multiple samples
tool_use_quality score low	Wrong tool selected or invalid arguments passed	Refine tool descriptions, instructions, or parameter documentation
LLM judge ignores image/audio in eval	get_text_from_content() skips non-text parts	Use custom metric with vision-capable judge (see references/multimodal-eval.md)

---

Debugging Example

User says: "tool_use_quality is low, what's wrong?"

1. Open the latest artifacts/grade_results/results_<timestamp>.html (or read the .json) and find the rubric verdicts the adaptive metric generated for the failing case.
2. Verify whether the agent selected the wrong tool, or called it with wrong arguments — the trace lives in artifacts/traces/.
3. Refine the tool's parameters, Python docstring description, or the agent's tool selection instructions to guide the model better.
4. Rerun agents-cli eval generate && agents-cli eval grade.
5. agents-cli eval compare <prev>.json <new>.json to confirm the score improved.

---

Proving Your Work

Don't assert that eval passes — show the evidence. Concrete output prevents false confidence and catches issues early.

• After running eval: Paste the scores table output so the user can see exactly what passed and failed.
• After fixing a failure: Show before/after scores for the specific case you fixed, and confirm no other cases regressed.
• Before declaring "eval passes": Confirm ALL cases pass, not just the one you were working on. Run agents-cli eval generate and agents-cli eval grade one final time.
• Before moving to deploy: Show the final agents-cli eval grade output with all cases above threshold. This is the gate — no exceptions.

---

Related Skills

• /google-agents-cli-workflow — Development workflow and the spec-driven build-evaluate-deploy lifecycle
• /google-agents-cli-adk-code — ADK Python API quick reference for writing agent code
• /google-agents-cli-scaffold — Project creation and enhancement with agents-cli scaffold create / scaffold enhance
• /google-agents-cli-deploy — Deployment targets, CI/CD pipelines, and production workflows
• /google-agents-cli-observability — Cloud Trace, logging, and monitoring for debugging agent behavior