Products on DataAgentX

Why Volcengine Routes Data Q&A and Deep Research Separately

Fri, 02 Oct 2026 00:00:00 +0000

“What was East China revenue yesterday?” and “Should we change product, audience, or outreach after repurchase fell?” are both data questions, but they are different jobs. The first is bounded and quickly verifiable. The second requires decomposition, multiple evidence types, and conditional judgment.

Volcengine separates intelligent data Q&A from deep research. The point is not the number of agents; it is giving different responsibilities different execution paths.

How a Data Agent Understands Average Order Value

Mon, 28 Sep 2026 00:00:00 +0000

“What is average order value by category over the last 30 days?” A model can easily produce valid SQL and an invalid business answer. Average order value may be paid amount divided by paid orders, exclude refunds, use buyers as the denominator, or count only fulfilled orders.

This is not primarily a SQL problem. The phrase has not yet been bound to executable meaning.

Why Microsoft Fabric Data Agents Stay Read-Only

Sat, 26 Sep 2026 00:00:00 +0000

Must an agent modify data or send tickets to be valuable? Fabric Data Agent takes a narrower position: it generates and executes read-only SQL, DAX, and KQL, leaving cross-system writes to external orchestration.

Identity comes before querying

Data access runs under the user’s Microsoft Entra ID and source permissions. Seeing a schema does not grant permission to read its data. Power BI semantic models remain subject to Read permission, RLS, and CLS. Enforcement stays in the data platform rather than in prompt instructions.

Three AWS Data Agent Routes Solve Different Problems

Thu, 24 Sep 2026 00:00:00 +0000

Amazon Quick, SageMaker Data Agent, and Amazon Bedrock are not three products to rank by feature count. They begin with different users and responsibility boundaries.

Route	Primary user	Main deliverable	Team responsibility
Amazon Quick	Business and BI users	Conversational answers, insights, stories, workflows	Topic semantics, access, answer review
SageMaker Data Agent	Analysts, data scientists, ML teams	Queries, code, analytical development	Environment, cost, code review, reproducibility
Amazon Bedrock	Application developers	Programmable agent capabilities	Tool contracts, identity, state, evaluation, UX

Quick: from Q&A toward business work

The current Quick experience emphasizes cross-dataset questions and agentic workflows, with multi-dataset Topics acting as a semantic layer. It is closest to a business application, but querying several datasets does not mean cross-domain metrics are already aligned. Topic design still controls terms, fields, and aggregation.

Google Cloud Data Agents Converge Three Kinds of Context

Tue, 22 Sep 2026 00:00:00 +0000

Knowing where a table lives is not the same as knowing how to answer a business question. Google Cloud describes Conversational Analytics data agents as a combination of context and tools: business information and data provide context; SQL, Python, and visualization tools perform work.

Three contexts have different authority

Data context identifies queryable objects, fields, relationships, and locations. Business context explains terms, metrics, filters, and verified queries. Conversation context retains the user’s scope, assumptions, and follow-up questions across turns.

Anthropic Skills Turn Analytics Practice into Workflows

Mon, 21 Sep 2026 00:00:00 +0000

Connecting a model to a warehouse does not create a dependable analyst. The model still needs the team’s methods for checking data, choosing calculations, presenting evidence, and reviewing a deliverable.

Anthropic defines a Skill as a folder of instructions, scripts, and resources loaded when relevant. That turns repeated prompt explanation into a versioned work protocol and allows deterministic code to handle steps where token generation is a poor substitute.

Inside OpenAI’s Data Agent: Context Before SQL

Fri, 18 Sep 2026 00:00:00 +0000

Two tables both contain user_id; why do they produce different active-user counts? Enterprise analytics often fails before SQL generation: the system chose the wrong table, timestamp, filter, or definition.

OpenAI’s January 2026 account of its internal data agent centers the design on context engineering. The article’s snapshot described more than 3,500 internal users, 600 PB of data, and roughly 70,000 datasets. The more transferable lesson is how the system narrows that search space.