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.

Answer Basis

Answers that AgentQ produces by running tools normally end with a short Answer basis note stating how the answer was produced:

The Model-synthesized answer label also covers a lighter case. A reply that drew on more than one listing tool, or that paged the same listing more than once, is labeled model-synthesized even when the reasoning over those results was simple. A routine exploration that lists datastores, then containers, then fields is labeled that way for that reason, which makes the label a common sight rather than a rare warning.

Two further cases carry the label for a different reason. A trust assessment or a guided workflow returns evidence and guidance for the model to reason from, so the verdict or plan the model writes is labeled model-synthesized even though the evidence came straight from a tool. And a reply that ran no tool at all, including a purely conversational one, carries the label because nothing verified it. In both cases the label states how the reply was produced, not that anything is wrong with it.

The note can carry two additional lines:

• Method: how the answer was produced. It appears on model-synthesized and could-not-verify answers, never on a direct tool result. On a model-synthesized answer it describes what AgentQ combined, compared, or calculated, states that the answer was assembled from several listing results or result pages, states that the model synthesized it from the evidence and guidance a tool supplied, or states that it was generated without a supporting tool result. On a could-not-verify answer it states that the requested result is not available through the tools.
• Limitations: what keeps the answer from being complete. On a direct answer it appears when the tool reported that more results exist than it returned. A model-synthesized answer can carry that same truncation warning, the completeness risk AgentQ stated for its own approach, a note that guidance tools supply evidence and a rubric rather than an authoritative answer, or a note that no tool directly verified the response. On a could-not-verify answer it carries the capability the available tools do not cover. A plain count answered with an exact total carries no Limitations line, even when the list shown alongside it was shortened.

Two kinds of reply carry no note: a request the topic guardrail declines, and the reply in which AgentQ pauses to ask for confirmation before a best-effort analysis, since that reply already explains the gap. The note comes back on the answer that follows once you agree.

Because the note is part of the response text, it stays with the message in the conversation history, after a reload, and in copies and PDF exports.

A model-synthesized label describes how the answer was built rather than claiming it is wrong. It tells you the result came from the model's own reasoning, over tool outputs or without them, rather than being returned directly by a tool, so give it the same review you would give an analysis assembled by hand.

Accuracy Reminder

Once a conversation has messages, a short line beneath them reads AgentQ can make mistakes. Worth a quick sanity check. It appears in the full-page chat, in the floating chat, and in the assistant panel inside the check and template dialogs. While the conversation is short the line stays just above the message box, and once the conversation outgrows the window it scrolls away with the messages.

The reminder is a standing prompt to review, not a judgment on any particular answer. For what an individual answer rests on, read its Answer basis note. For how to put a mistake right, see What happens if AgentQ makes a mistake?.

Documentation Answers

A copy of this User Guide ships with every Qualytics release, and AgentQ answers product questions from it. Ask how a feature, setting, or rule type works and AgentQ searches that bundled copy before answering from the model's general knowledge, whichever AI provider is configured. This applies even when the question names a third-party product: setting up an identity provider, a datastore connection, or a notification integration with Qualytics is still answered from the guide.

The answer quotes or paraphrases the relevant guide section and includes a link to the exact page. That link opens the guide hosted by your own deployment, so both the answer and the page it opens match the version you are running. When the guide does not cover a topic, AgentQ says so and points you to the Qualytics support team instead of guessing.

The assistants in the check and template dialogs follow the same rule: questions about how the product works, such as filter syntax or what a setting controls, are answered from the guide with the page link included.

Confirmation Before Best-Effort Analyses

Some requests have no tool that answers them directly, and deriving an answer would mean fetching and comparing large amounts of data (for example, comparing every datastore against every schedule, or proving that something never happens). In those cases AgentQ pauses before running that analysis, explains that no tool answers the request directly, and asks whether to proceed. The question sets out the Proposed approach it would take and, where one applies, the Completeness risk that comes with it. AgentQ may already have run a quick lookup while working out what you asked for, so the tool call timeline above the question can show a few completed steps.

• Nothing further runs until you agree. AgentQ closes the question by asking you to reply yes.
• Only a short, direct confirmation is recognized: yes, yes please, yes try it, yes try the proposed derivation, proceed, please proceed, proceed with the proposed derivation, go ahead, go ahead with the proposed derivation, or try it. Capitalization and punctuation do not matter, but other wordings such as sure, ok, or do it are not recognized, and neither is an accepted phrase with extra words added to it, such as yes, go ahead. A reply AgentQ does not recognize does not count as approval, so the analysis does not run and the question comes back.
• Your agreement is remembered for the rest of the chat. The first one covers the derivations that come later in the same conversation, so AgentQ stops asking, and the question says so when it is put to you. There is no time limit, and it survives a break or a page reload. A new chat starts without it.
• AgentQ always asks whenever the analysis draws on more than one tool's results, carries a completeness risk it has stated, or its own restatement of the request or of the proposed approach uses wording that points at a wide or absence-based analysis, such as all, every, compare, infer, never, or without. It can also ask on its own initiative when it judges an analysis expensive or open-ended, so even a narrow request can be put to you for approval.
• AgentQ does not ask before every derivation. A narrow calculation over one tool's results runs without a prompt, and a reply that simply drew on several listings is only recognized as derived after the fact. In both cases the answer still carries the Model-synthesized answer label, which is your signal to review how it was built.

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.

Conversations Inside the Check and Template Dialogs

Besides the full-page chat and the floating chat, there is a third place conversations live: the assistant panel docked inside the quality check dialog and the check template dialog. Conversations you have there behave differently from the other two surfaces, so it is worth knowing the rules.

Opening the Panel

The panel is opened from a narrow strip of round buttons along the right edge of the dialog. One of them carries the AgentQ mark, and hovering it names the assistant you are about to open, reading Check Assistant in a quality check dialog and Template Assistant in a check template dialog. The strip also holds a button for the description editor. While the panel is open the AgentQ button drops out of the strip, and the top of the strip becomes Hide panel, which closes it again.

You can also widen or narrow the panel by dragging its left edge, on a large enough screen. The width applies to the dialog you are in and returns to the default the next time you open one.

You often do not need to open the panel at all:

• When you open the dialog to create a check or template, the panel is already open.
• When you edit an existing one, the panel starts closed, unless that item already has a conversation, in which case it opens on its own. Because the item's conversations are looked up after the dialog appears, the panel can open a moment after the rest of the form.

Cloning is the exception to the first rule. Clone reshapes the dialog you already have open rather than opening a new one, so the panel keeps whatever state it was in: if it was hidden while you were editing, it stays hidden. The conversation does restart empty, with the wording used for a new check.

Once you show or hide the panel yourself, your choice takes over from those defaults for that dialog type. The check dialog and the template dialog each remember their own choice, so hiding the assistant while working on checks does not hide it for templates. Each preference is remembered by the browser you are using, so it survives reloads and restarts but does not follow you to another browser or device.

The one exception is a workspace where AgentQ has not been set up yet. There the panel stays closed when you edit an existing check or template, whatever you chose last, and you open it yourself if you want the link to the setup screen.

Before your first message the panel offers two starter prompts. Selecting one sends it straight away. The prompts change depending on whether you are creating or editing, and most also differ between the check and the template dialog.

One Conversation Per Item

A conversation held in this panel belongs to the item it was opened from, and to you. Reopening that item's dialog restores the most recently updated conversation you had about it, with the earlier messages already in place. A colleague opening the same check sees their own conversations, never yours.

Creating a new check or template is the exception, because the item does not exist yet. Until you save, that conversation is an ordinary AgentQ chat and appears in your Chats list like any other. When you click Save, it becomes attached to the check or template you just created, and from then on it resumes inside that item's dialog. If you close the create dialog without saving, the conversation stays an ordinary chat.

Once a saved check or template has at least one conversation, the panel header turns into a switcher listing your conversations about that item, with the one you are in highlighted. Each is labeled with the opening words of its first message and how long ago it was last active. Selecting another opens it, once the current reply has finished. To begin a fresh conversation instead, use New chat in the panel header, which appears as soon as the current conversation has messages and is unavailable until the current reply has finished. The conversation you leave stays in the switcher.

There is no switcher while you are creating something, however long the conversation gets, because there is no item yet to hold earlier conversations. In that case, and on a saved item you have never chatted about, the header simply reads Check Assistant or Template Assistant. New chat is still offered, so you can start over without leaving the dialog.

What the Assistant Can See

Each message you send carries a snapshot of the form as it currently stands, so the assistant can reason about what you have already filled in. The two dialogs send different snapshots, because a template holds fewer settings than a check.

In a quality check dialog, the snapshot covers the datastore and container, the rule type and its properties, the fields, the filter, the coverage, the description, the tags, the owner and the default anomaly assignee, the check's status, the field used for custom anomaly descriptions, any additional metadata, and whether a template is attached and locked. It also flags a check that is archived and a metric check that has already been asserted.

In a check template dialog, there is no datastore, container, fields, or ownership to send. The snapshot covers the rule type and its properties, the filter, the coverage, whether the template is locked, the description, the tags, and any additional metadata.

In both, if your last attempt to save failed, the error travels with it too, which is what lets the assistant help you work out why.

That snapshot is never shown in the conversation: your message bubble shows only the words you typed, both at the time and when you reopen the conversation later. Unlike the floating chat, this panel shows no context badge, because the form beside it is the context.

Long Conversations & Memory

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

How Compression Works

AgentQ keeps a long chat lean so the model stays fast and inside budget. Recent turns stay exactly as you wrote them, older tool activity is replaced with a short placeholder, and past a certain length the chat gets a stored summary that travels with every later turn.

Nothing from early in a long chat is lost to you. It stays available to AgentQ through that summary, even if you come back days later. For the exact thresholds, see Conversation Context and Compression.

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 Chat.

For the chat list itself, with its Chats and Archived tabs, the recency headings it groups rows under, and what the message input shows when a chat is read-only, see The Chat Interface. For worked scenarios that put all of this together, see AgentQ Examples.

See Also