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How to use Unknown to implement Db2 migration support

The source reports Db2 support completed in less than two days with twenty-seven merged PRs and test coverage equivalent to PostgreSQL; two escalations required a human.

Added cockroachlabs.comAgent: Unknownvia MOLT Sinai
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Prompt to try

Editorial adaptation, not the source author’s exact prompt. Replace bracketed inputs and review tool permissions first.

Implement [source database] support in [migration tools] from [GitHub issue]. Decompose the work into dependent issues for types, row iteration, Fetch, Verify, Convert, CI and fixtures. Review plans before coding, split oversized issues, file discovered gaps, require tests and reviews before merging, and escalate [defined blockers] to a human.

What happened

The source reports Db2 support completed in less than two days with twenty-seven merged PRs and test coverage equivalent to PostgreSQL; two escalations required a human.

Reported by rafiss on Blog

How the agent did it

  1. Open a GitHub issue describing Db2 migration support.
  2. Decompose the request into dependency-ordered sub-issues.
  3. Split oversized workups and file issues for discovered defects.

Where a person stepped in

The pipeline required planning and review handoffs; two of nine escalated issues reached humans, and the source says no human wrote the code.

A person set the goal and reviewed the result.

Cockroach Labs describes using its MOLT Sinai coding pipeline to add IBM Db2 source support to its database migration tools. The work included a schema converter, Fetch and Verify paths, a vendored ANTLR grammar, a CI Docker image, and more than ten thousand lines of test fixtures. The supplied excerpt identifies the pipeline but does not name its underlying agent model.

The process began with a GitHub issue describing the request. A planning agent split it into fifteen dependent sub-issues, ordered from foundation and types through row iteration, Fetch, Verify, Convert, CI, and test data. Two issues were divided further after their own workups found them too large. Agents also filed another dozen issues about their work, including fixture gaps, a type-mapping bug, and an isolation-level correction.

The source reports thirty-two sub-issues by closure, twenty-seven merged pull requests, fifty-five reviewer send-backs, and nine escalations, two to a human. It says coverage matched PostgreSQL, its best-tested dialect, and that no human wrote the code. The reported Db2 effort ran from a Wednesday evening to Friday afternoon. Who it's for: migration-tool teams that can define dependency plans, require reviews and tests, and handle explicit escalations before merging changes.

Prompt
Suggested prompt (written by Agents In Use, not the author's)

Source: Blog · rafiss

Frequently asked questions

How did the agents divide the Db2 request?

A planning agent created fifteen sub-issues with an explicit dependency graph. Two were decomposed again, and agents filed additional issues about gaps and defects in their own work.

What review and escalation results were reported?

The parent effort ended with twenty-seven merged PRs, fifty-five reviewer send-backs and nine escalated issues, including two escalations to a human. Coverage was reported equivalent to PostgreSQL.

More details

Remaining steps

  1. Implement and return work through review and escalation.
  2. Merge reviewed pull requests and compare dialect test coverage.

About this account

The pipeline planned and implemented Db2 support through specialized coding and review roles, filing further issues as defects and gaps appeared.

Platform: Blog. Attribution: https://www.cockroachlabs.com/blog/experiment-running-hospital-code/. Prompt: Suggested (written by Agents In Use, not the author's). Sourced by the Agents In Use pipeline.

Credit: rafiss on Blog

Source published 9 Oct 2026

Source checked 14 Oct 2026

Tools and topics

MOLT Sinai · GitHub · MOLT · ANTLR · Docker

Database migration · Db2 · Agent reviews

Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.