Evaluation on DataAgentX

Before Adjusting a Fee Rate, Identify Which Transactions Will Be Affected

Thu, 22 Oct 2026 00:00:00 +0000

If a payment fee rate is reduced from 14 basis points to 1 basis point, how much can a merchant save in a month?

While this appears to be a multiplication problem, the actual challenge lies before the multiplication: determining which transactions are affected. This analysis recalculates two “hard” problems from DABstep, with a focus on defining the scope and performing independent verification.

Hotel Data in Practice: Why Brand Rankings Change

Tue, 20 Oct 2026 00:00:00 +0000

Why doesn’t a brand with an average 5-star rating always rank first on the hotel brand leaderboard?

This walkthrough follows the complete pipeline using public benchmark questions: Data → Question → Solution → Validation. The focus is not merely on calculating the answer, but on understanding how qualification thresholds and denominator selection alter conclusions.

Data and Questions

The dataset is InfiAgent-DABench, which evaluates the ability to truly execute analysis and deliver results starting from CSV files, natural language questions, and constraints. This fixed question set contains 257 questions referencing 52 CSV files; here, we use only hotel_data.csv, comprising 1,057 rows and 9 columns, covering 7 cities and 103 non-null brands, with each row representing a hotel record.

How to Build a Data Agent: A Working Map from 23 Articles

Sun, 04 Oct 2026 00:00:00 +0000

After surveying many Data Agent products, a team beginning implementation still faces five basic questions: What work should the system own? Which data should it use? How may it execute? How will we prove the result? Who remains accountable when it fails?

Those questions are more durable than a vendor list. They connect definition, architecture, autonomy, evaluation, and rollout.

The diagram below maps those questions onto an operating path: how a task enters the system, how semantics constrain planning and execution, and how evidence, approval, read-back, and feedback close the loop.

How Kuaishou Scaled Its Data Agent Beyond 10,000 Weekly Users

Sun, 20 Sep 2026 00:00:00 +0000

A data agent reaching more than 10,000 weekly active users matters because it has moved into data production, business diagnosis, and recurring delivery—not because a chat box received more prompts. Kuaishou’s September 19, 2026 technical presentation described a progression from bounded workflows to a unified agent entry point.

The presentation reported more than 10,000 weekly users, over 80 percent penetration in core data-analysis and data-operations roles, more than 2,000 Skills, and over 600,000 evaluation records. These are company-reported snapshots, not independently audited results or promises that transfer to another organization. The system relationships behind the numbers are more useful than the headline itself.

How to Read Data Agent Benchmarks Before Trusting the Ranking

Mon, 14 Sep 2026 00:00:00 +0000

A first-place Data Agent is not automatically the best procurement choice. A leaderboard says which submissions performed better under a particular task set, data environment, tool policy, budget, and scoring rule. An enterprise needs to know whether the system can deliver under its definitions, permissions, cost, and failure conditions.

The most useful way to read a ranking is backward through its measurement chain.

OpenAI Data Analytics Skills as Reviewable Work Protocols

Sun, 13 Sep 2026 00:00:00 +0000

“Why did conversion fall?” A demo agent can quickly locate a table, write SQL, draw a line, and suggest several causes. Delivery exposes the harder questions: What is the denominator? Is the current period complete? Did channel mix change? Are the “causes” measured contributions or a plausible story?

The Data Analytics Skills are useful because they turn analytical responsibility, handoffs, and completion criteria into reusable work protocols—not because of one special prompt.

If the SQL Runs, Can You Trust the Data Agent Answer?

Thu, 10 Sep 2026 00:00:00 +0000

A successful query, attractive chart, and complete report do not independently make an answer trustworthy. The agent may have selected the wrong source, bound the wrong metric, executed correct SQL over incomplete data, and then described correlation as causation.

One accuracy score compresses these failures into a number and hides high-cost errors. A better evaluation follows the analysis chain layer by layer and sends each failure back to the component that can repair it.

A Seven-Layer Architecture for Production Data Agents

Mon, 07 Sep 2026 00:00:00 +0000

“Why did new-customer conversion fall this month?” sounds like a SQL question. It is not.

Before touching a warehouse, a useful Data Agent has to establish what new customer means, which conversion window applies, whether the metric changed recently, which data the requester is allowed to see, and what evidence would make an explanation credible. A query can run perfectly and still answer the wrong question.

Production Data Agents therefore need more than a language model and a database connector. They need a chain of responsibilities that turns ambiguous intent into a result another person can inspect. The seven-layer model below is a way to design that chain—and, when it breaks, to locate the failure.

Data Agent Levels L0–L5: Autonomy Transfers Responsibility

Sun, 06 Sep 2026 00:00:00 +0000

One system writes SQL. Another invokes SQL and Python, observes errors, and replans. The second system has more tools, but tool count alone does not make it more autonomous. The real questions are: Who sets the goal? Who organizes the work? Who is authorized to act? Who verifies the result? Who recovers—and remains accountable—when it fails?

Data Agent levels are therefore best understood as a transfer of responsibility, not a feature checklist. At each level, the agent inherits more operations, judgment, and risk.