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Agent Evaluation & Deployment

🤖 Vertex AI Agent Builder 12m 250 BASE XP⌨ HANDS-ON LAB

Production Verification & Automated Evaluation

Before deploying autonomous agents to customer-facing or mission-critical channels, enterprises must rigorously evaluate safety, reasoning fidelity, and tool call precision. Vertex AI Agent Builder provides Playbooks and the Vertex AI Evaluation Service for end-to-end quality assurance.

Evaluation Paradigms on Vertex AI
  • Pointwise Metrics: Evaluates single-turn outputs for Groundedness (verifying claims against citations), Safety (harm categories), and Coherence.
  • Pairwise AutoSxS (Side-by-Side): Uses frontier Gemini models as an impartial judge to score new agent iterations against production baselines, providing statistical win rates and rationale.
  • Tool Call Evaluation: Validates whether the agent invoked the optimal tool with correct schema parameters and handled edge-case error payloads gracefully.
from vertexai.preview.evaluation import EvalTask, MetricPromptTemplateExamples

# Configure automated evaluation task for agent responses
eval_dataset = "gs://my-bucket/eval_golden_set.jsonl"

eval_task = EvalTask(
    dataset=eval_dataset,
    metrics=[
        "groundedness",
        "question_answering_quality",
        "tool_call_accuracy"
    ],
    experiment="agent_v2_launch_candidate"
)

result = eval_task.evaluate(
    model="gemini-2.0-flash",
    prompt_template="{instruction}
Input: {user_query}"
)
print(f"Summary Metrics: {result.summary_metrics}")

Once verified, agents can be deployed instantly to multichannel targets including Dialogflow CX telephony gateways, Google Workspace / Chat apps, or custom frontend web applications via the Interactions API.

⌨ HANDS-ON LABEvaluate Agent Output with Vertex AI Evaluation Service
⭐ +150 XP

Initialize an automated evaluation job computing groundedness and ROUGE-L metrics across agent dialogue traces.

1Initialize an EvalTask using the vertexai.evaluation Python library.
lab-sandbox — simulated environment
INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$
OBJECTIVE 1 / 1 — type "hint" if stuck
SYNAPSE VERIFICATION
QUERY 1 // 2
What feature in Agent Builder allows you to simulate and test expected user paths?
Simulators
Playbooks
TestBots
Vertex Eval