What Training Data Do AI Coding Agents Need?

What a coding agent actually does

A coding assistant that suggests the next line is helpful, but a coding agent is asked to do more. It is given an objective, often written like a ticket, and it has to find the relevant files, understand how the system fits together, make changes in several places, run tests, read the errors and try again until the work is done.

Each of those steps is a skill. Reading an unfamiliar codebase, deciding where a change belongs, writing a test that proves a fix, responding to a reviewer's comment: these are learned from examples of people doing them well, and from examples where something went wrong and was corrected.

Coding agents are also expected to work inside existing systems rather than start from a blank page. Most real software work is maintenance: adjusting behaviour, fixing defects, upgrading dependencies and keeping old code running. That makes long-lived commercial codebases a better reflection of everyday engineering than small demonstration projects.

Why source code alone is not enough

A snapshot of source code shows what a system looks like today. It does not show why it looks that way, what problem each change solved, or how the team decided between alternatives. For an agent that has to make good decisions, that missing context matters.

This is why private repositories are valuable well beyond the code itself. The surrounding history explains the intent behind the code and shows how changes were planned, reviewed, tested and shipped.

• Historical PRDs and planning documents that state the objective in business terms.
• Issues and tickets that describe the problem as a user or teammate saw it.
• Commit history that shows the sequence of edits made to reach a working solution.
• Code reviews and pull request discussions that capture judgement, trade-offs and corrections.
• Tests and CI results that show how correctness was checked.
• Release records that confirm what actually shipped.

The value of a linked history

The richest examples connect these pieces. An issue describes a bug; a branch and a series of commits attempt a fix; a pull request is opened and a reviewer asks for changes; tests fail and then pass; the change is merged and released. Together, that chain gives an objective, a sequence of tool use and decisions, and a verifiable outcome.

That last part is especially important. Many training approaches for coding agents rely on being able to check whether a task was completed correctly, often by running tests. A repository with a meaningful test suite and a clear record of which change fixed which problem provides exactly that kind of check.

Tests do not need to be perfect or comprehensive. Even partial coverage gives a way to confirm that a specific change did what it was meant to do, and records of failing and then passing builds in CI logs add further evidence of how the problem was solved.

Which codebases tend to be most useful

Usefulness depends on several factors rather than size alone. Long-lived projects with consistent use of issues and pull requests tend to be richer than repositories where everything was committed straight to the main branch. Specialised domains, such as financial systems, healthcare integrations, embedded software or large enterprise applications, are less common in public data. Legacy stacks, including older Java, .NET or COBOL systems, can be valuable because modernising such systems is a common real-world task.

Completeness also counts. A repository where tickets, commits and reviews are linked is easier to learn from than one where history was squashed or moved between tools without the links surviving.

Learning from mistakes as well as successes

It might seem that only the clean, final version of each change matters. In practice, the journey is often more instructive. A pull request where the first attempt broke a test, a reviewer pointed out a missed edge case and the author fixed it shows an agent how to recover from errors, which is one of the hardest things for coding agents to do well.

Reverted commits, follow-up bug fixes and long review threads all carry this kind of signal. That is one reason teams are often surprised to hear that their imperfect history has value. A repository that shows how problems were noticed and corrected can be more useful than one where everything appears to have worked first time.

Questions to ask about your own repositories

A few quick questions can help you judge which of your repositories are worth raising in a first conversation.

• Did the team use issues and pull requests consistently, or commit directly to the main branch?
• Are there automated tests, and do they still run?
• Are PRDs, specs or design docs stored somewhere that can be linked to the code?
• How many years of history are there, and did it survive any migrations between Git hosts?

Preparing a repository for a partnership

Before any repository is considered, confirm you own it or have the right to license it, which is a real question for agencies and outsourcing firms. Then think about what needs to be removed: secrets and credentials, personal information in commit metadata or comments, customer data in test fixtures, and any third-party code with restrictive licence terms.

With Momentra, you decide what is in scope, and no sample is reviewed until an NDA covering confidentiality, permitted use, retention and security is signed. Professional anonymization with purpose-built tools is used to detect, remove or replace sensitive information before further use, and transfer happens securely under the signed agreement.