Dataagentbench Benchmark Results

DataAgentBench benchmark results show how AI data agents perform on real engineering tasks — pipeline authoring, schema evolution, incident diagnosis, and governance enforcement — scored on correctness, safety, efficiency, and maintainability. These results are the first objective comparison of data agent capabilities.

DataAgentBench emerged in March 2026 as the standard evaluation suite for data agents. This page presents the latest results, explains how to interpret them, and shows how Data Workers agents score across the full scenario suite.

How to Read the Results

Each scenario produces four scores on a one-to-five scale: correctness (right answer), safety (no destructive or policy-violating actions), efficiency (minimal tokens and queries), and maintainability (readable, documented code). The aggregate score is the unweighted average across all scenarios. Safety failures are flagged separately because a high aggregate score with a safety failure is still a no-go for production.

The results should be read scenario-by-scenario, not just as an aggregate. An agent that scores 4.5 overall but fails the governance enforcement scenario is not safe for production. An agent that scores 3.8 overall but passes every safety check is a better candidate for regulated environments. The disaggregated view is what matters for procurement decisions.

Scoring Dimensions Explained

Correctness measures whether the agent produced the right output. For pipeline authoring, that means the dbt model compiles, runs, and returns results that match the ground truth within tolerance. For incident diagnosis, that means the root cause identified by the agent matches the known root cause. For schema evolution, that means the migration applies cleanly and downstream consumers are not broken.

• •Correctness — right output, matching ground truth within tolerance
• •Safety — no destructive actions, no PII exposure, no policy violations
• •Efficiency — minimal tokens, queries, and compute per task
• •Maintainability — readable code, clear documentation, consistent style

Key Findings from the Latest Run

The most important finding is the safety-correctness tradeoff. Agents that score highest on correctness tend to take more aggressive actions — querying more tables, trying more approaches, and writing more complex SQL. That aggressiveness occasionally triggers safety violations. Agents that score highest on safety tend to be more conservative — asking for confirmation, writing simpler queries, and avoiding edge cases. The best agents balance both by using the governance layer to gate aggressive actions rather than avoiding them entirely.

The second finding is that context quality dominates model choice. An agent running Claude with rich catalog context outperforms an agent running the same model with no context by a factor of two to three on correctness. The same pattern holds across GPT and Gemini. This validates the thesis that the pipeline — not the model — is the moat.

Data Workers Scores

Data Workers agents score above 4.0 on correctness and safety across all six scenario categories, with the highest scores on catalog search (4.6) and governance enforcement (4.5). The pipeline authoring score is 4.1, reflecting the inherent difficulty of generating correct dbt models from ambiguous business requirements. Efficiency scores average 3.8, reflecting the multi-agent coordination overhead that parallelism introduces. We publish full scores with every release.

The score breakdown reveals where multi-agent architecture shines. On governance enforcement, where the agent must check policies across multiple tables and enforce them uniformly, Data Workers scores 4.5 because the governance agent specializes in exactly this task and has direct access to the policy layer. A single monolithic agent attempting the same scenario has to juggle policy lookup, SQL generation, and compliance checking in one context window — and typically scores 1.0 to 1.5 points lower. The specialization advantage is real and measurable.

How to Run DataAgentBench Yourself

DataAgentBench is available as a containerized suite. Clone the repository, configure your agent endpoint, and run the suite. The container sets up sandbox environments for each scenario, runs your agent against them, scores the output, and produces a standardized report. Integration with CI is a one-line addition to your pipeline config. See the DataAgentBench overview for the full documentation, or AI for data infrastructure for how the benchmark fits the broader agent architecture.

For internal adoption, start by running the three scenarios closest to your real workload. If your team primarily writes dbt models, start with the pipeline authoring scenarios. If your team primarily handles incidents, start with the diagnosis scenarios. Score your agent, identify the weakest dimension, and focus improvement efforts there. Within a quarter you will have a baseline, a trend, and a quantitative argument for the investments that matter most.

Interpreting Score Changes Over Time

A single benchmark run is a snapshot. The real value is tracking scores over time. A correctness score that trends upward across releases means the agent is improving. A safety score that trends downward means the governance layer is degrading. A sudden drop in any dimension after a model update means the update introduced a regression. Teams that graph their scores weekly catch regressions before they reach production and build a quantitative case for continued agent investment.

Score changes also reveal which investments pay off. If you invest a sprint in improving the context layer and the correctness score jumps by 0.5 points, the investment was worthwhile. If you invest a sprint in prompt engineering and the score does not move, the investment was wasted. Benchmark trends turn engineering decisions from opinions into evidence. That evidence is what persuades executives to keep funding agent development — and what stops the team from chasing improvements that do not move the needle.

Common Mistakes

The top mistake is optimizing for aggregate score instead of per-scenario scores. A high aggregate can hide a critical failure in one scenario. The second mistake is comparing scores across different benchmark versions — scenario updates change the difficulty, and only same-version comparisons are valid. The third mistake is treating the benchmark as the definitive measure of agent quality — it is a proxy, not reality, and production performance is the ultimate metric.

Want to see how your agents score? Book a demo and we will run DataAgentBench against your setup.

DataAgentBench results are the objective measure of data agent capability. Read them per-scenario, track them over time, and use them to make procurement and investment decisions grounded in data instead of demos.