How do I test an agent I simulate and monitor in Maxim?

Connect your deployed agent to TestMu AI through its chat, web app, or API endpoint, point us at the docs behind its knowledge, and let it auto-generate scenarios. An AI evaluator then drives your agent like a real user across thousands of scenarios, scoring every answer and tool call with a clear pass or fail.

Why test agents if I already use Maxim for simulation and observability?

Maxim helps your team simulate, evaluate, and monitor the agents you build, and that pre-release and production work is valuable. TestMu AI adds an independent, outside-in QA gate on top: it drives the deployed agent like a real user, scores conversations against your own criteria, and returns a single go-live verdict. Changing a prompt, swapping a model behind your gateway, or re-indexing a knowledge source can quietly break an answer that worked yesterday, and an external check catches invented facts, ungrounded answers, wrong tool calls, and lost context before a user hits them.

Does TestMu AI check that my agent's RAG answers are grounded in my sources?

Yes. A common failure is the LLM answering from its training instead of your indexed documents, or stitching together stale chunks after a sync. TestMu AI runs scenarios drawn from your own source docs and flags answers that are not grounded in the knowledge, cite policies or numbers that do not exist, or contradict the retrieved context.

Can it verify the tool and retrieval calls in my agent?

Yes. Beyond the prose, TestMu AI checks that the agent picks the right action, calls the right retrieval, SQL, or connected API node, and passes correct parameters. For API-backed agents it also validates the structured JSON output against your downstream schema.

Can I automate agent regression testing in CI/CD?

Yes. TestMu AI supports scheduled runs using preset frequencies or full custom cron expressions with IANA timezone support. You can also trigger runs from your CI/CD pipeline so every change to a prompt, tool, or re-indexed knowledge source is regression tested before you ship.

Does this replace Maxim?

No, it complements it. You keep using Maxim to simulate, evaluate, and observe the agents your team builds, and you use TestMu AI as an independent QA layer that evaluates the deployed agent from the outside, the way a real user or downstream system would. Pair it with KaneAI and agent testing to prove the agent behaves correctly on every release.

Test the AI Agents You Build, Simulate, and Monitor with Maxim

Deploy autonomous AI evaluators against your chat agents, RAG agents, and tool-using workflows across thousands of scenarios. Catch ungrounded answers, wrong tool calls, and lost context before they reach users.

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Deep Dive into Maxim Testing

Agent Conversations

Tool and Action Calls

Scenario Generation

Go-Live Assessment

Test Your Agent Conversations

Score every reply from the agents you simulate and monitor in Maxim across 9 quality metrics, including hallucination detection, knowledge grounding, and conversation flow.

9 Quality Metrics

Score bias, hallucination, completeness, context awareness, response quality, and conversation flow on every chat turn.

Grounding Checks

Confirm answers come from your retrieval sources, not policy or pricing the LLM invented under the hood.

Multi-Turn Memory

Push the agent through follow-ups and clarifications to catch where it loses the thread between turns.

Complete Maxim Testing Coverage

Confidence by Evaluation Volume

HIGH (100+ evaluations), MEDIUM (50-99), LOW (20-49), VERY LOW (below 20). Confidence calibrates to how many scenarios you run.

9 Quality Metrics on Every Response

Bias, hallucination, completeness, context awareness, response quality, flow, user satisfaction, file handling, and file accuracy, ideal for document-heavy RAG agents.

4-Dimension Go-Live Assessment

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

Pass/Fail Analysis Output

Pinpoint every match and discrepancy in your agent's answers and tool calls, tracked as Pass, Fail, or Partial against your criteria.

Built for Every Layer of Agent QA

Project and Environment Management

Separate staging and production agents into test projects and scope variables, with bulk creation.

Test Profiles and Personas

Run support, sales, and back-office personas against the agent with reusable test data.

Custom Validation Criteria

Define evidence-based pass/fail rules per scenario, including grounding and tool-call checks, with High/Medium/Low confidence tracking.

Security and Infrastructure

Execute via HyperExecute with optional secure tunnels for VPC or firewall-restricted agent API endpoints.

Scheduling Engine

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

Observability and Reporting

Monitor agent quality across runs with unified dashboards, exportable reports, and real-time quality trends.

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Success Stories of TestMu AI (Formerly LambdaTest)

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reduction in test execution time

“HyperExecute is a highly reliable test execution platform and has excellent customer support.”

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Sr. Engineering Manager

Some Love from our Customers

As Best Egg expanded its product offerings and entered new markets, we knew our old testing infrastructure couldn’t keep up.
With support from Tenny Agustin, our Engineering Operations Lead, we modernized our approach with

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Best Egg

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Excited to Share My Learning Journey with Kane AI & Lambda Tool!
I'm pleased to announce that I've recently gained hands-on experience exploring Kane AI through the Lambda Tool and it’s been a fantastic journey of upskilling!

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See how is #Futureready to enable blazing-fast test orchestration seamlessly integrated with organizations' existing CI/CD platforms, using #Microsoft Azure.

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