How do I test a Vertex AI Agent Builder agent?

Connect your Agent Builder endpoint to TestMu AI, ingest the docs you grounded it on or connect a knowledge source, and let it auto-generate scenarios. An AI evaluator then chats with your agent like a real user across thousands of scenarios, checking grounded answers and tool calls and scoring each response with a clear pass or fail.

Why should I test agents built on Vertex AI Agent Builder?

Agent Builder ships a Gemini-powered agent fast from Agent Studio, the ADK, or an Agent Garden template, but once live it answers customers, quotes your policies, and runs tools against your systems. Edit a playbook, swap a model in Model Garden, or reindex a Vertex AI Search data store, and a conversation that worked yesterday can quietly start hallucinating or calling the wrong tool. Independent testing catches wrong answers, ungrounded facts, broken function calls, and lost context before a user does.

Can TestMu AI test grounding and tool calls in my Agent Builder agent?

Yes. We verify that responses are grounded in the intended Vertex AI Search data store or BigQuery source rather than the model's pretraining, and that the agent invokes the correct ADK tool or function with valid parameters. We flag hallucinations, retrieval from the wrong source, malformed tool arguments, repeated calls on identical inputs, and answers that hide a failed API call instead of escalating.

How does automated Vertex AI Agent Builder testing work?

An AI evaluator agent interacts with your Agent Builder agent like a real user. It sends structured prompts across scenarios, captures every turn and tool call, and scores them on 9 quality dimensions including hallucination, grounding, context awareness, and completeness. Results report per scenario with confidence-weighted pass/fail verdicts so you see exactly which intents and tools are failing.

Can I run Agent Builder regression tests in CI/CD before deploying to Agent Engine?

Yes. TestMu AI supports scheduled runs using preset frequencies or full custom cron expressions with IANA timezone support, and you can trigger runs from your CI/CD pipeline. Run the suite on every change to a playbook, prompt, or data store and catch regressions before you promote to the Agent Engine runtime.

Does this replace the evaluation tools in Vertex AI Agent Builder?

No, it complements them. Agent Builder gives you traces and metrics for the agent you are building; TestMu AI is an independent QA layer that evaluates it from the outside, the way a customer would. Keep building in Agent Builder and use KaneAI and agent testing to prove the agent behaves correctly before and after every release.

Test the AI Agents You Build on Vertex AI Agent Builder

Built an agent on Vertex AI Agent Builder? Deploy autonomous AI evaluators to test it across thousands of conversations, catching ungrounded answers, wrong tool calls, and lost context before you go live.

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Deep Dive into Vertex AI Agent Builder Testing

Chat Conversations

Tool and Function Calls

Scenario Generation

Go-Live Assessment

Test Vertex AI Agent Builder Chat Conversations

Score every conversation on 9 quality metrics and catch the Gemini-powered answers that contradict your data store or miss the question.

Grounding and Hallucination

Confirm answers trace to your Vertex AI Search data store and BigQuery sources, not the model's pretraining.

Real Support Scenarios

An HR agent quotes the wrong leave policy, an order-status agent invents a tracking number. Both get flagged before release.

Go-Live Verdict

Get a Green, Yellow, or Red production-readiness call before you promote to the Agent Engine runtime.

Complete Vertex AI Agent Builder Testing Coverage

Confidence by Evaluation Volume

HIGH (100+ evaluations), MEDIUM (50-99), LOW (20-49), VERY LOW (below 20). Confidence calibrates to how much you have exercised the agent before trusting a verdict.

9 Quality Metrics on Every Grounded Answer

Bias, hallucination, completeness, context awareness, response quality, flow, user satisfaction, file handling, and file accuracy, scored against your data store.

4-Dimension Go-Live Assessment

Each run scores Functional Completeness, Quality Standards, Risk Profile, and Operational Readiness, each weighted at 25%, before you promote to Agent Engine.

Pass/Fail Analysis on Tools and Answers

Pinpoint every match and discrepancy in responses and tool calls, tracked as Pass, Fail, or Partial against the criteria you define.

Built for Every Layer of Vertex AI Agent Builder QA

Project and Environment Management

Create test projects, separate dev and prod agent endpoints, and scope variables with bulk creation.

Test Profiles and Personas

Inject reusable test data and run scenarios across a pre-built or custom persona library to pressure-test every intent.

Custom Validation Criteria

Define evidence-based pass/fail rules per scenario, including expected tool calls, with High/Medium/Low confidence tracking.

Security and Infrastructure

Execute via HyperExecute with optional secure tunnels for VPC-restricted or firewalled Agent Engine endpoints.

Scheduling Engine

Automate runs using preset frequencies or full custom cron expressions with IANA timezone support.

Observability and Reporting

Track grounding, tool-call, and quality trends across runs with unified dashboards and exportable reports.

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