Conversations, Responses & Context

This page covers how AgentQ processes your messages and what happens during a response. It also explains how context from your current page is injected automatically and how AgentQ manages long conversations.

Starting a Conversation

Type your request in the message box at the bottom of the chat and press Enter to send it. Here are some examples of what you can ask:

Smart Suggestions

When you open a new conversation, AgentQ generates 3 personalized prompt suggestions based on your data assets. The suggestions are not static. They are tailored to the containers in your workspace with the most active anomalies, so the prompts are immediately useful instead of generic.

Suggestions are designed around the 5 guided workflow tools: investigating anomalies, analyzing trends, interpreting quality scores, generating quality checks, and transforming datasets. These appear as Workflow steps in the response, and each suggestion is formatted with explicit container and datastore names so you can send it directly without editing.

The suggestions render as a single-view carousel labeled Suggested prompts above the message input. Navigate with the left and right chevrons, jump directly with the indicator dots, and use the X of Y counter to see where you are in the list. Clicking a suggestion sends it immediately as your message; there is no separate "confirm" step.

When the chat is opened with a specific asset in context (a badge above the input), the carousel is hidden so it does not compete with the scoped question you already came here to ask. Suggestions also disappear once the conversation has messages, and reappear when you start a new session.

Understanding Responses

When AgentQ processes your request, you see its progress unfold in three phases:

1. Thinking. AgentQ analyzes your message, decides which tools to call, and builds an execution plan. A "Thinking..." indicator appears while this happens.
2. Tool execution. Each action it takes appears as an expandable step labeled with the operation name (for example, Quality Scores, List Quality Checks, Create Quality Check). Click any step to see the exact input parameters sent and the raw output returned.
3. Response. The final answer is rendered in Markdown with tables, code blocks, and formatted text as appropriate.

You can continue the conversation to refine results, ask follow-up questions, or request additional actions.

Tool Step Labels

The step labels you see during a response correspond to specific platform operations. The table below covers the most common labels for exploration, quality checks, insights, and guided workflows:

Tool Call Timeline

While AgentQ works, every tool step for the current response is grouped into a single Used N tool(s) timeline above the response.

• The timeline auto-expands during streaming so you can watch each step run, then auto-collapses about one second after the response finishes. Click the timeline to reopen it at any time.
• Each step shows a status dot: a subtle animation while the step is running, and a filled dot after it completes. The completion color reflects the outcome (success, warning for partial results, or negative for a failure).
• Expanding a completed step reveals a Results panel with the Input the agent sent and the Output it received. Long outputs are shortened and end with a ... (truncated) marker. Scrolling reveals what fits in the panel, not the removed portion. If you need the full output, ask AgentQ to summarize or repeat the specific detail.

Asset Cards Below Responses

When AgentQ creates a new asset for you, a clickable card appears below the response with a short caption and the asset name (for example, "Created #341960 • isNotNull" for a quality check, or a small "Operation" caption above a bold "Scan #7741" label for an operation). Clicking the card takes you straight to that asset's page.

Cards cover newly created quality checks, quality check templates, computed tables, computed files, computed joins, computed fields, and completed operations (Sync, Profile, Scan, Export, Materialize). If AgentQ produces more than three cards in one response, only the first three are visible with a + N more button at the end.

Updates to existing assets do not produce cards. The change still appears in the tool call timeline above, and the affected view refreshes automatically (see Auto-Refresh After Responses below).

Deletions do not appear as cards either. When AgentQ removes something you were looking at, it redirects you instead. See When AgentQ deletes what you're viewing below.

Auto-Refresh After Responses

Views tied to the assets AgentQ just changed refresh automatically. You never need to reload the page after a response.

• Newly created or updated assets appear in the affected listings, side panels, and trees as soon as the tool step completes.
• Long-running operations that transition to running also trigger refreshes, so their live status appears without a manual reload.

The refresh is scoped to the surfaces the response affected (only the checks list for a check, only the datastore tree for a container, and so on), not a full-page reload. If you are on a page unrelated to what AgentQ just did, nothing on your side changes.

When AgentQ Deletes What You're Viewing

If the assistant deletes the asset you are currently looking at, Qualytics redirects you to a safe destination so you are not left on a dead page:

The redirect only fires when the deleted asset matches the page you are on. If you navigated away before AgentQ finished the deletion, no redirect happens; the affected listings on your new page still refresh normally.

Message Actions

Each message includes action buttons:

Context-Aware Conversations

AgentQ automatically detects the page you are on and injects the relevant asset context into the conversation. This means you can ask direct questions without repeating which datastore, container, or anomaly you mean.

When you open the floating chat widget from a page, the injected context appears as a badge above the input box (icon plus a short asset label) before you send your message, so you can see exactly what AgentQ is working with.

Long Conversations & Memory

AgentQ automatically manages conversation length to keep the context window efficient without losing important history.

How Compression Works

AgentQ keeps your conversation lean so the model stays fast and within budget:

1. Recent turns stay intact. The last 4 turns (user + assistant pairs) are always kept exactly as you wrote them.
2. Older turns get a short recap. Before the conversation grows large, older turns are condensed into a compact summary (around 3,000 characters). Tool calls and tool outputs from those turns are dropped to save space.
3. At around 10 messages, the recap becomes a smarter summary. Once the session reaches 10 messages, AgentQ replaces the short recap with a structured summary (also up to 3,000 characters) generated by the model. This summary stays with the session even if you come back days later.
4. Big tool outputs get trimmed. In older rounds (anything before your last 2 messages), tool results are trimmed to 300 characters, so a large output like a full container list isn't re-sent on every turn.

Information from early in a long conversation is not lost. It stays available to AgentQ as part of the summary throughout the session.

Background Streaming

If you navigate away from the AgentQ page while a response is being generated, the stream continues in the background. The full response is waiting for you when you return to that session.

For other interface tips like stopping a response or pasting large content, see Chat Interface Tips.

Session Persistence

Every conversation is saved. When you resume a session, AgentQ restores the full message history and the conversation summary, so it picks up exactly where you left off, even across days or sessions.

For step-by-step instructions on resuming a session, see Resume a Conversation.

Example Conversations

Investigating a Data Quality Alert

You: We got an alert about anomalies in the transactions table. What's going on?

AgentQ: I found 3 anomalies from the last scan. The most significant is a "Not Null" check failure on the merchant_id field. 247 records have null values where this field is expected to be populated. This started appearing after the batch that loaded at 2:00 AM. The affected records all have source_system = 'legacy_import'. Would you like me to show you the specific check that's failing or help investigate the source of these null values?

Setting Up Quality Monitoring for a New Dataset

You: We just connected a new PostgreSQL database called "ecommerce_prod". Can you help me understand what's in there and set up some basic quality checks?

AgentQ: I found 12 tables in the ecommerce_prod datastore. The main ones appear to be customers, orders, order_items, products, and inventory. Would you like me to describe the schema for any of these in detail?

You: Yes, tell me about the orders table and suggest some quality checks.

AgentQ: The orders table has 15 fields including order_id, customer_id, order_date, ship_date, status, and total_amount. Based on the schema, I'd recommend these quality checks:

• order_id should be unique
• customer_id should not be null
• total_amount should be positive
• ship_date should be after order_date when both are present
• status should be one of your valid status values

Want me to create these checks?

You: Yes, and also look at order_items. I think the same line shouldn't appear twice per order.

AgentQ: For order_items, the natural identifier is a pair like (order_id, line_item_id) rather than either column on its own. order_id repeats across line items in the same order, and line_item_id resets per order. I'll add a Unique check on both fields together so the combination is enforced as a composite key. Want me to apply it now?

Building Cross-System Analytics

You: I need to analyze customer lifetime value but our customer data is in Salesforce (synced to Snowflake) and our transaction data is in our PostgreSQL operational database. Can you help?

AgentQ: I can create a computed join that combines data from both sources. I'll join the customer table from your Snowflake datastore with the transactions table from PostgreSQL on customer_id. What fields do you need in the result? At minimum I'd suggest customer details, total transaction count, total spend, and first/last transaction dates.

You: That sounds right. Also filter out any test customers.

AgentQ: I've created a computed join called "customer_lifetime_value" that combines the data and excludes records where the customer email contains "test" or the customer_type is "internal". The join is now available as a container in Qualytics. Would you like me to set up quality checks on this new dataset or run a profile to understand the data distribution?