How to Read Data Agent Benchmarks Before Trusting the Ranking
A six-part measurement-chain checklist for understanding benchmark scope, data, runtime, evaluators, failure taxonomy, and transfer to enterprise work.
Knowledge path
Tech
Architecture, semantics, tool use, evaluation, security, and production patterns for trustworthy Data Agents.
DataAgentX Tech explains the systems behind reliable agentic data work—from the semantic layer and execution harness to observability, evaluation, and human control.
When Is a Data Agent Report Actually Delivered?
Delivery contracts for business readers, analysts, and systems—from evidence packages and accessible artifacts to approval, idempotent submission, read-back, and feedback.
OpenAI Data Analytics Skills as Reviewable Work Protocols
The 15 Data Analytics Skills form a responsibility map from routing and business context through measurement, validation, communication, and publishing.
How Far Data Agent Governance Must Extend
A control chain for identity, retrieval, tools, sandboxes, output, actions, prompt injection, excessive agency, approvals, audit, and incident response.
If the SQL Runs, Can You Trust the Data Agent Answer?
A layered evaluation framework covering source choice, semantics, execution, analysis, delivery, offline regression, shadow testing, and observability.
Why Data Agent Tasks Still Fail When the Agent Can Plan
A practical design for complexity routing, explicit state, step contracts, checkpoints, replanning, recovery, and meaningful multi-agent boundaries.
Why Agent-Generated SQL Must Not Run Directly
A controlled execution gateway for validating, authorizing, limiting, sandboxing, verifying, and auditing SQL, code, and external tool calls.
Why Stronger Data Agents Need a Semantic Layer
Schema describes data shape. This guide shows how metrics, entities, dimensions, business rules, and verified examples turn meaning into executable contracts.