Open Data Infrastructure
Agentic Data Write Approval Queues
How approval queues govern agentic writes across proposed changes, table branches, policy checks, owner review, and promotion.
The moment an agent proposes a data write, the architecture needs a waiting room.
Approval queues make writes reviewable
Read-only agents can still cause trouble. Write-capable agents raise the stakes. A proposed write can change a table, update a customer record, alter a feature value, or create downstream context that other systems trust.
An approval queue gives the proposed write a controlled state. The queue should capture the agent identity, source evidence, target table or record, proposed diff, policy checks, owner review, replay plan, and promotion decision.
Open tables make safer review possible
Open table formats and catalogs give teams useful primitives for review. Branches, snapshots, metadata, and lineage can help separate proposed change from promoted change. They do not replace human review, but they give the review something concrete to inspect.
A good approval queue should show what changes, why the agent proposed it, which policy allowed the proposal, and what would happen if the change were promoted.
Core idea: agentic write approval is change management for data products, not a thumbs-up button.
The ODI pattern separates proposal from promotion
Open Data Infrastructure gives write approval queues a place in the control loop. Proposed changes belong with contracts, policies, lineage, owner accountability, and evaluation evidence.
For adjacent context, read agentic AI write paths with human review, agentic data contract tests, and Iceberg row-level deletes for agent safety.
What breaks first
- The agent writes directly because the review step is slower than the demo.
- The approval queue stores text summaries instead of replayable diffs.
- Policy checks approve the tool call but not the actual data change.
- Promotion succeeds without updating downstream evaluations or lineage.
Questions to ask
Ask which writes must enter the queue, which evidence is required, who can approve promotion, and how rollback works. Ask whether approval decisions are tied to data product ownership instead of generic application ownership.
Sources to start with
These primary sources anchor the technical claims in this guide.
- Apache Iceberg branching and tagging documentation
- Apache Iceberg table specification
- Model Context Protocol tools specification
- Open Policy Agent policy language documentation
The safest agentic write is the one that can wait, explain itself, and earn promotion.