Quick Answer
Google Cloud now presents Agent Evaluation as a generally available part of Gemini Enterprise Agent Platform. The useful change is a connected quality loop: teams can define cases, generate or import traces, score agent behavior, inspect failures, and use related metrics for production monitoring.
That does not make an agent reliable by default. A score is only useful when the cases represent real tasks, the metric reflects the failure you care about, and a human can trace the verdict back to evidence.
What Is Generally Available?
Google’s current launch post places Agent Evaluation beside Agent Simulation and Agent Observability. It documents online monitors for production performance and behavioral drift, plus prebuilt metrics, custom Python, LLM-as-a-judge metrics, and adaptive rubrics developed with Google DeepMind.
The product documentation describes three operating modes:
| Mode | Primary question | Typical cadence |
|---|---|---|
| Rapid evaluation | Did a prompt, tool, or model change improve this build? | Frequent during development |
| Test-case or offline evaluation | Does this version still pass a fixed regression suite or historical sample? | Before release and in CI/CD |
| Online monitoring | Is quality changing on sampled production traces? | Continuously after deployment |
Simulation can create initial cases and multi-turn traces when production history is unavailable. It is a test-data method, not a substitute for representative production evidence.
The Evaluation Lifecycle
Google’s evaluation documentation describes a six-part loop:
- Define cases with tasks, context, expected outcomes, or a conversation plan.
- Run the cases against a versioned agent.
- Capture traces containing model inputs, responses, and tool calls.
- Compute prebuilt or custom metrics.
- Inspect rubrics, verdicts, trace details, and failure clusters.
- Change the agent and rerun the same acceptance suite.
Keep the case set and metric version stable when comparing releases. If both change at once, a higher score may reflect an easier test rather than a better agent.
What to Measure
Do not reduce agent evaluation to answer fluency. A useful suite separates at least four dimensions:
- Task success: whether the user’s observable goal was completed.
- Tool-use quality: whether the agent chose the right tool, arguments, and schema.
- Trajectory quality: whether the sequence was causally valid, efficient, and robust.
- Safety and grounding: whether the result respected boundaries and remained supported by available context.
Add deterministic checks for facts that have one correct answer. Use model-based raters for nuanced qualities, and audit a sample of their verdicts against human review.
A Safe Adoption Plan
1. Start with a bounded service
Choose one agent, one version, and two or three high-value tasks. Record the deployment, model, instructions, tools, region, and data boundary.
2. Build a known-answer suite
Include normal cases, ambiguous requests, authorization failures, tool timeouts, malformed results, and requests the agent should refuse. Keep a human-approved expected outcome for consequential cases.
3. Compare before and after
Run the same suite against the current and candidate versions. Review regressions individually; an average can hide a critical safety or authorization failure.
4. Add production monitoring
After release, sample traces with documented filters and privacy controls. Use online scores as detection signals, then investigate the underlying trace before changing the system.
The offline versus online guide explains this handoff in detail. The simulation and drift guide covers pre-production stress tests and production alerts.
Evidence Boundaries
Google documents the product’s capabilities and GA status. Those are provider facts. They do not independently prove that a particular deployment is accurate, safe, compliant, or cost-effective.
Before rollout, verify:
- current project, region, API, and account access;
- telemetry and Cloud Storage data paths;
- retention, access control, and sensitive-data handling;
- evaluator model and metric versions;
- sampling cost and latency;
- human escalation for critical verdicts.
Treat adaptive or LLM-based grades as fallible measurements. The adaptive rubrics guide shows how to review and freeze generated criteria for a fair comparison.
Official Sources
- What’s new in Gemini Enterprise Agent Platform
- Agent evaluation overview
- Evaluate your agents
- Agent Platform optimization overview
Source check: August 1, 2026. Verify the live Google Cloud documentation, console, quotas, and data controls before implementation.