In a multi-repo setup, what are good patterns for providing an AI agent with context from other internal repositories?

Q: In a multi-repo setup, what are good patterns for providing an AI agent with context from other internal repositories?

Multi-repository architectures are common in enterprises, but they complicate AI agent workflows by scattering relevant code and documentation across isolated repos. The goal is to bridge these silos without overwhelming the agent’s context or introducing maintenance burdens. Effective patterns range from quick fixes for ad-hoc needs to scalable, automated solutions that ensure consistency and freshness.

1. Use Git Submodules for Structured Access

For ongoing projects requiring sustained access to dependencies, integrate external repos as Git submodules. This creates a local “monorepo” view by nesting the dependent repositories within your main project. All code becomes accessible via a unified file system, allowing the agent to navigate and reference files seamlessly.

Submodules simplify discovery but require careful management. Historically, they could be cumbersome to set up and update, but modern tools make this easier—leverage an AI agent itself to generate the precise Git commands for initialization, cloning, and synchronization. This approach works well for stable, long-term dependencies where full code visibility is beneficial.

2. Implement Automated Documentation Distillation

Draw from DevOps principles to automate context extraction. Configure CI/CD pipelines, such as GitHub Actions, to run on every commit in a dependency repo. These workflows “distill” essential information — API details, key functions, or architectural overviews — into a compact, machine-readable file.

Push the updated file to your main project’s repository for easy inclusion in the agent’s context. This method delivers a reliable, always-current snapshot without duplicating entire codebases. It’s ideal for dynamic environments, as it minimizes manual intervention and scales across multiple dependencies.

3. Develop Custom CLI Tools

Avoid bloated, generic integrations by building lightweight, purpose-built scripts. For example: use the GitHub API to create CLI commands that fetch specific artifacts, like files from a docs folder in another repo. Document these tools in a simple file and provide clear instructions for the agent on invocation.

This targeted toolkit keeps operations efficient and private, focusing only on high-value interactions. It’s particularly useful when you need granular control, such as querying metadata or pulling recent changes, without exposing the full repo.

4. Opt for Manual Copy-Paste in One-Off Scenarios

For temporary or exploratory tasks, the fastest solution is to manually copy relevant files or snippets into a dedicated folder in your current project. This injects immediate context without setup overhead.

While not scalable, it’s a pragmatic starting point to prototype agent behaviors or handle urgent needs. Transition to automated patterns as the use case matures to avoid repetition and errors.

Select patterns based on project duration and complexity: submodules or distillation for persistent needs, custom tools for precision, and copy-paste for speed. Combining them — e.g., distillation feeding into custom scripts — often yields the most robust setup.