AgentQ FAQ

Frequently asked questions about AgentQ, the AI assistant built into the Qualytics platform.

General

What is AgentQ?

AgentQ is the AI-powered assistant integrated into the Qualytics platform. It uses the Model Context Protocol (MCP) and your configured LLM provider to give you natural-language access to shared, governed context and the actions available through your Qualytics permissions. You can ask it to explore datastores, create quality checks, investigate anomalies, run operations, send notifications, create tickets, and perform other supported tasks. Review its responses and resulting changes before relying on them.

How does AgentQ work?

AgentQ connects to your configured LLM provider and uses a set of MCP tools to interact with the Qualytics platform on your behalf. When you send a message:

1. A scope guardrail checks whether the request relates to data quality or the Qualytics platform.
2. Your configured LLM interprets the request using your prompt and relevant Qualytics context.
3. AgentQ selects from the Qualytics actions available to your account.
4. It performs those actions one at a time or in sequence as needed.
5. Results are streamed back with progress indicators for each step.

Review the completed steps, generated explanations, and any platform changes before using the result.

Is my data sent to the LLM?

AgentQ sends your prompt, relevant asset context, and results from the tools used to fulfill your request to your configured LLM provider. What a tool can return is limited by its capabilities and your Qualytics permissions. Depending on the task, those results can include source-record values (actual rows from your source systems). The model does not connect directly to your source systems, and AgentQ's tools do not return data beyond what the requesting user is authorized to access in Qualytics. Your provider handles the information it receives according to its own privacy and data processing policies.

Will AgentQ act on requests outside of data quality?

AgentQ is designed to decline requests outside data quality and the Qualytics platform. A scope guardrail checks whether a request relates to data quality, governance, databases, anomalies, transformations, or Qualytics. Because the guardrail uses an LLM, it can occasionally misclassify a request. Rephrase a relevant request with more specific data quality context if needed.

Setup

What LLM providers are supported?

AgentQ supports the Qualytics-managed provider (Beta) and a range of external providers, including OpenAI, Anthropic, Google Gemini, Amazon Bedrock, Groq, Mistral, DeepSeek, Cohere, Ollama, OpenRouter, and Perplexity. See the external-provider list with example models in the Add Integration guide.

Do I need my own LLM API key?

No. You can choose the Qualytics provider (Beta), which does not require an API key, model choice, or custom endpoint. You can also connect a supported external provider with your own credentials.

How is the provider configuration validated when I save it?

Qualytics sends a minimal request to the selected provider to confirm connectivity before saving. For an external provider, this test also validates the supplied credentials. The Qualytics-managed provider is tested with the deployment's managed connection and does not require your organization to supply an API key.

What is the Base URL field for?

The Base URL is an optional custom endpoint for OpenAI-compatible providers that self-host or proxy models. Use it for providers like Ollama (local), OpenRouter, LiteLLM proxy, or any other provider that exposes an OpenAI-compatible API at a custom URL.

Can AgentQ search the Qualytics User Guide?

AgentQ does not provide general web search. It includes a bundled User Guide search capability for finding relevant content on userguide.qualytics.io, available across supported AgentQ providers. Review the linked documentation before relying on an answer for a business-critical decision.

Can multiple users configure different LLM providers?

No. The AgentQ configuration is deployment-wide. A single Manager or Admin selects the provider for all users with the Member role or higher. External providers also require a model and credentials; the Qualytics-managed provider does not. Only one active provider configuration exists at a time per deployment.

What happens if a Manager changes the LLM provider?

Existing conversation history is preserved. Sessions are stored independently of the LLM configuration. The new provider takes effect immediately for every user on their next message. Different models may behave differently for the same prompts, so results may vary across providers.

What happens if I don't configure an LLM?

Without an LLM configured, AgentQ displays a "No LLM Integration Configured" message with a Configure LLM button. The platform is fully functional otherwise; only AgentQ's chat capabilities are unavailable. Follow the Add Integration guide to get started.

Usage

How do I open AgentQ?

Use any of these entry points:

• Click AgentQ in the left sidebar for the full-page chat interface.
• Use the floating action button in the bottom-right corner of any page. Press the Q key to toggle it from anywhere on the page (except while typing in an input).
• Open the AgentQ assistant inside a quality check or check template dialog to draft settings from a plain-language requirement. Apply the proposal to the form, review the highlighted fields, and save only when the result is correct.

What are the smart suggestions?

When you start a new conversation, AgentQ generates 3 personalized prompt suggestions using the LLM, based on your actual containers and active anomalies. These highlight common workflows like investigating anomalies, creating quality checks, and analyzing trends. Click any suggestion to use it as your opening message.

Suggestions are hidden when you open AgentQ from a page where context is already injected (e.g., from an anomaly page).

How does context injection work?

When you open AgentQ from a page that has relevant data (a datastore, container, field, quality check, or anomaly), AgentQ automatically receives that asset's identity as context. This context appears as a badge above the input box (icon + asset name). You can then ask questions like "Explain this anomaly" or "What checks exist here?" without specifying which asset you mean.

The context is included with your first message and is visible through the Context action button after the message is sent.

Can I have multiple conversations?

Yes. AgentQ supports full session management. You can start new conversations at any time, switch between sessions from the history sidebar or the floating chat dropdown, search sessions by title, and rename, archive, restore, or delete sessions.

Does AgentQ remember previous conversations?

Yes. Each session maintains its message history. In long sessions, AgentQ keeps recent messages in detail and summarizes older context so the conversation can continue within the model's limits.

If you notice AgentQ losing context in a very long conversation, starting a new session for a fresh context window is the most reliable approach.

What happens if I navigate away while AgentQ is generating?

The response continues streaming in the background. When you return to that session, the completed (or in-progress) response is waiting for you. You can see which sessions are still generating from the loading indicators in the session list.

Can AgentQ make changes to my platform configuration?

Yes. AgentQ can create and update quality checks, create computed assets (tables, files, joins, fields), promote assets across datastores, trigger operations (sync, profile, scan, export, materialize), manage tags, send notifications, and create tickets. All changes are tracked in the audit trail and marked with AgentQ co-authorship alongside your user identity.

Can I export AgentQ responses?

Yes. Click the Export as PDF button below any assistant message to download a formatted PDF. You can also copy any response to your clipboard.

Can I upload files to AgentQ?

Yes, when your active LLM provider supports binary content. The chat input shows an Attach icon for PDF, Word (.doc, .docx), Excel (.xls, .xlsx), CSV, TSV, JSON, XML, plain text, and Markdown files. Limits: one file per message, up to 20 MB per file. The button is shown for Anthropic, Google Gemini, Amazon Bedrock (Claude models), OpenAI, Azure OpenAI, and Heroku. See Attach a File for full details.

Are there usage limits?

Yes. To ensure fair usage and predictable costs:

If you hit the rate limit, wait a moment and try again. If you consistently exceed token limits on a single request, try breaking it into smaller focused steps.

What happens if AgentQ makes a mistake?

Ask AgentQ to propose or apply a specific correction in the same conversation. For example: "That check is wrong. The field should be not-null, not unique. Please update it." Whether AgentQ can make the correction depends on the available tools and your permissions. Review the completed tool steps and the updated object to confirm the result.

AgentQ does not provide a universal undo. If a change was already applied, use the platform interface or ask AgentQ to perform a specific follow-up action that its tools support, such as deleting a newly created check. Confirm the target and impact before applying a destructive action, then verify the resulting platform state.

How much does it cost to use AgentQ?

Costs depend on the configured provider and your organization's commercial terms. With an external provider, your organization pays that provider based on usage. The Qualytics-managed provider does not require a separate LLM account or API key. Ask your deployment administrator or Qualytics contact which terms apply to your environment.

To reduce costs, use specific, focused prompts and scope requests to a particular datastore or container rather than platform-wide queries. See Best Practices for detailed guidance.

Can I share a conversation with another user?

Not directly. Conversations are private to your user account. To share findings, use the Export as PDF button below any assistant message to download a formatted PDF you can share externally.

How long are conversations stored?

Conversations persist indefinitely until you delete them. Archived conversations are also kept until explicitly deleted. See Delete a Conversation for instructions on permanent removal.

What languages does AgentQ support?

AgentQ can understand and respond in any language your configured LLM model supports. The platform interface is in English, but you can write prompts in your preferred language and AgentQ will respond in kind.

MCP Integration

What is MCP?

The Model Context Protocol is an open standard that enables AI assistants to securely connect to external data sources and tools. Qualytics implements an MCP server that exposes its data quality functionality to any compatible client.

Can I connect external AI clients to Qualytics?

Yes. Beyond the built-in AgentQ, you can connect ChatGPT, Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, Amazon Q Developer, and any MCP-compatible client directly to the Qualytics MCP server using your Personal API Token. See the Connecting External AI Clients guide for step-by-step instructions for each client.

Which external AI clients are supported?

Qualytics provides step-by-step setup guides for the following MCP clients:

Any other MCP-compatible client that supports Streamable HTTP transport can also connect. See the Connecting External AI Clients guide for full instructions.

Do external clients have the same capabilities as AgentQ?

External MCP clients share the same tool set as AgentQ. They connect to the same MCP server and have access to the same tools for exploration, quality checks, transformations, anomalies, operations, and integrations. However, features specific to the Qualytics UI (context injection, smart suggestions, session management, PDF export) are only available in the built-in AgentQ chat.

What is the difference between AgentQ, the MCP server, and the agentic endpoints?

All three use the same underlying MCP tools and capabilities. They differ only in how you access them.

What tools are available?

AgentQ and external MCP clients use the same tool catalog. The data and actions available in a session depend on your Qualytics permissions and the relevant feature configuration.

Security

What safeguards apply when using AgentQ in production?

AgentQ includes safeguards that reduce risk, but they do not replace human review or your organization's governance requirements. Suitability depends on your use case, configured LLM provider, permission model, and data-handling requirements. Use least-privilege access and verify proposed and completed changes, especially for business-critical or regulated data.

• Scope guardrail: Requests outside data quality and Qualytics are declined.
• Query restrictions: Computed asset queries are validated. Only SELECT statements and CTEs are permitted; statements that insert, update, delete, or change database objects are blocked.
• Context protections: Content returned by Qualytics tools is processed before it is sent to the LLM.
• Usage limits: Request, concurrency, timeout, and token limits help contain resource usage.
• Co-authorship tracking: All platform changes are audited with AgentQ attribution.
• Permission enforcement: AgentQ respects the same roles and team permissions as the rest of the platform. It can only access data and actions available to your user account.

AgentQ sends prompts, relevant context, and tool results to your configured LLM provider. Review that provider's privacy, retention, and data-processing terms before enabling AgentQ for sensitive data.

Troubleshooting

AgentQ is not responding

• Verify your LLM configuration is active in Settings > Integrations.
• For an external provider, check that the API key is valid and the provider account has sufficient credits.
• You may have hit the rate limit (10 requests/minute). Wait briefly and try again.
• If you exceeded a token limit, try breaking the request into smaller steps.

I'm getting unexpected results

• Be specific about datastore and table names in your requests.
• Use the context injection feature: open AgentQ from the relevant page so it automatically knows which asset you mean.
• If AgentQ creates something incorrect, ask it to propose or apply a specific update, then verify the resulting platform state.
• For complex multi-step tasks, break them into sequential steps.

Tool calls are failing

• Some operations require specific permissions. Verify your Qualytics user has the necessary access rights.
• If a datastore operation fails, confirm the Qualytics service account has access to that datastore.
• Click the tool step in the response to expand it and read the specific error in the Output section.

The response was cut off mid-way

You may have hit a timeout (5-minute agent execution limit) or token limit for a single request. Try:

• Breaking the request into smaller, more focused prompts.
• Starting a fresh session and asking the same question with more specific context.
• Using a more capable model (e.g., GPT-4o or Claude Sonnet) if you're using a smaller model.

Computed table/file/join quality checks fail immediately after creation

Computed assets require a profiling operation to complete before quality checks can be created on them. AgentQ can trigger the Profile operation and wait for it to finish as part of the request. Confirm that the operation completed before relying on the new check. If you're creating checks programmatically through the API, wait for the Profile operation to complete before creating checks.

Smart suggestions are empty or not showing

Suggestions are generated based on your top 5 containers with active anomalies. If no anomalies exist in your datastores, suggestions may be generic or not appear. Run a scan on your datastores to generate anomalies, or simply type your request directly.

My request was declined as off-topic

AgentQ's topic guardrail occasionally misclassifies edge-case requests. If your request is genuinely related to data quality or the Qualytics platform, try rephrasing it with more explicit context. For example, mention a specific datastore, container, or quality check in your message.