Iceberg Puffin Statistics for Agents

Agents should not guess their way through a lakehouse. They need evidence before they write a query that can burn compute, miss policy boundaries, or return a technically correct answer from the wrong slice of data.

The practical problem

Apache Iceberg already gives teams a strong table format boundary: snapshots, manifests, schema evolution, partition specs, and metadata tables. Puffin adds another useful piece to that boundary. The Puffin spec defines a file format for table statistics and indexes that can be referenced from Iceberg metadata.

That matters for agent query planning because generated SQL is only as good as the evidence the agent can see. An agent that only knows column names can choose a join, filter, or aggregation that looks valid while ignoring distribution, cardinality, skew, missing values, and file layout. In human terms, it is reading the menu without seeing the kitchen.

Statistics should become planning evidence

Puffin files should not be treated as trivia attached to a table. They should become part of the planning context exposed to humans, services, and agents. Before an agent writes a query, the context layer can expose which statistics exist, which snapshot they belong to, when they were produced, and which fields they cover.

That makes the query path more honest. If the table has current statistics for a field, the agent can use them to choose a safer plan. If the statistics are stale or missing, the agent should say that uncertainty out loud instead of pretending the table is fully understood.

Core idea: Puffin statistics are useful when they help the planner explain risk, not when they sit in metadata as another artifact nobody reads.

Where this fits in Open Data Infrastructure

Open Data Infrastructure is not only about open files. It is about preserving control and meaning as data moves across engines, catalogs, policies, and AI systems. Puffin statistics sit in that control layer because they describe table behavior without forcing every consumer into one proprietary optimizer.

The catalog still matters. The catalog should decide which agents can see statistics, which statistics are trustworthy, and which policies apply when the agent uses those statistics to shape a query. A statistic about a sensitive column can be operationally useful and still inappropriate to expose to every tool.

What breaks first

Statistics are generated, but nobody ties them to the snapshot used by the agent.
The agent sees table structure but not the evidence needed to estimate query cost or risk.
Distribution signals leak sensitive business facts because policy only protected row access.
Different engines produce different planning behavior, and the platform has no shared evidence layer.

Questions to ask

A practical platform review should ask which statistics the agent can inspect, who produced them, how freshness is tracked, and what happens when the evidence is missing. The answer should connect to Iceberg metadata evidence, AI-ready context tests, and the broader Open Data Infrastructure control model.

If agents are allowed to write SQL, they need metadata that can push back. Puffin can help provide that pushback.

Sources to start with

These primary sources anchor the technical claims in this guide.

The agent should ask the table what it knows before it asks the warehouse to prove a guess.

ODI hub Article library Use the scorecard Iceberg evidence Context tests

Apache Iceberg Puffin Statistics for Agent Query Planning