How do I test a Stack AI agent?

Connect your published Stack AI workflow to TestMu AI through its chat, web app, or API deployment, point us at the docs behind your Knowledge Base, 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 should I test agents built on Stack AI?

Stack AI ships a no-code agent fast as a website chatbot, internal copilot, or API, but once published it quotes your policies, drafts documents, and calls into your CRM and databases. Re-indexing the Knowledge Base, tweaking a prompt, or rewiring a node can quietly break an answer that worked yesterday. Independent testing catches invented facts, ungrounded answers, wrong tool calls, and lost context before a user hits them.

Does TestMu AI check that my Stack AI agent's RAG answers are grounded in my Knowledge Base?

Yes. A common Stack AI 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 Base, cite policies or numbers that do not exist, or contradict the retrieved context.

Can it verify the tool and Knowledge Base node calls in my workflow?

Yes. Beyond the prose, TestMu AI checks that the agent picks the right action, calls the right Search Knowledge Bases, SQL, or connected API node, and passes correct parameters. For Form and API deployments it also validates the structured JSON output against your downstream schema.

Can I automate Stack AI 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, node, or re-indexed Knowledge Base is regression tested before you republish.

Does this replace Stack AI's own tooling?

No, it complements it. TestMu AI is an independent QA layer that evaluates your agent from the outside, the way a real user or downstream system would. You keep building workflows in Stack AI and use KaneAI and agent testing to prove the agent behaves correctly before and after every release.

Test the No-Code Agents You Build on Stack AI

Deploy autonomous AI evaluators against your Stack AI chat assistants, RAG agents, and document 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 Stack AI Testing

Chat Assistants

Tool and Action Calls

Scenario Generation

Go-Live Assessment

Test Stack AI Chat Assistants

Score every Stack AI chat assistant reply 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.

RAG Grounding Checks

Confirm answers come from your connected Knowledge Base, not policy or pricing the LLM invented.

Multi-Turn Memory

Push the assistant through follow-ups and clarifications to catch where it loses the thread.

Complete Stack AI 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 Stack AI agents.

4-Dimension Go-Live Assessment

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

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 Stack AI QA

Project and Environment Management

Separate staging and production Stack AI workflows 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 Stack AI 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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