You asked a simple question in Slack: "How does the login flow work today?" Two hours later, you received links to three different repositories, a recently merged PR, and an outdated API contract.
Developers use AI agents to debug code. Product managers and tech leads should use AI agents to debug context — and that is exactly what ivar enables for people who never touch a terminal.
If you are a product manager, a designer, a tech lead, or anyone who works around a codebase rather than inside it, you probably hit the same wall: the engineering team maintains five repositories, and you have no single place to understand what lives where, how things connect, or what changed. Ivar does not require you to write code. It gives you a structured view of a multi-repo codebase that an AI agent can reason over — and that means you can ask questions in plain language and get structured answers.
When an engineering team adopts ivar, they create a Hall — a single directory that describes every repository the project depends on, which branch to use, and what instruction files carry the team's conventions.
Think of the Hall as a table of contents for the codebase. It lists each repository by name, points at the right branch, and records the relationships between them. You do not need to clone anything, run any commands, or understand the file structure of five separate repos.
The Hall is a file, and it lives in version control. If the team adds a repository, the Hall changes. If a repository is renamed or removed, the Hall changes. The Hall is the source of truth for how the codebase is organised.
Once the Hall exists, ivar creates a view directory — a single workspace that contains every repository on the right branch, with the agent's instruction files already set up. An AI agent opened in that directory can read across all the repositories at once.
This is where you benefit without writing code. You can ask the agent:
You are not reading source code. You are having a conversation with an agent that has read access to everything, and the agent translates the technical details into language you can act on.
The typical alternative is asking an engineer to write a summary, run a report, or explain a dependency graph. That is expensive, slow, and stale the moment it is delivered.
With ivar, the agent in the view directory can generate that summary on demand. The Hall already carries the context — which repos exist, how they relate, what the team's conventions are. The agent does not need to be re-introduced to the codebase every time.
This is particularly useful for:
Ivar does not replace your project management tool, your CI pipeline, or your engineering team. It is a local tool that gives an AI agent the context it needs to answer questions about a multi-repo codebase. The agent does the reasoning; the Hall provides the structure.
Ivar also does not discover repos automatically. The engineering team defines the Hall — which repos, which branches, which instruction files. Once that is set, everyone on the team can use it, including people who never touch a terminal.
If your engineering team already uses ivar, ask them to show you the Hall. It is a small file, and understanding it gives you the vocabulary to ask the agent useful questions.
If your team does not use ivar yet, the [quickstart guide](https://ivar.run/docs/quickstart) explains how to create a Hall. The investment is small — it takes less than an hour to set up — and the return is a single place where everyone on the team, technical or not, can understand how the codebase is organised.