The engine that gives our agents the whole repository
Our code-intelligence engine gives our agents the shape of a whole repository before they touch it, and hands them the context a change needs within a budget they can afford.
- IndustryDeveloper tooling
- EngagementInternal engine, built for our own agents
- StatusIn use on our own work, still being extended
Built for our own agents first
This is our own tool: the part of our toolchain that decides what an agent gets to see before it touches anything. It gives an agent the project context to inspect relevant definitions and references before changing code.
The constraint that shaped everything is the reasoning budget. An answer too large to read is worse than a smaller answer that fits, so every answer is sized to what the agent can use. The point is to reserve the budget for the work.
The problem was ours
Agents working on large codebases need relevant project context before making changes.
An open file is not a codebase
An agent asked to change a function could see the function and the file around it. What called it, what it called in turn, and which parts of the project would notice the change all sat outside the frame. The change was reasoned about in isolation, and reviewed the same way.
Reading a whole project to find one thing
Without a map, finding the right place means opening files and discarding them. Every discarded file has already been paid for out of the same budget the actual work needs, and on a large repository that cost arrives before any useful thinking starts.
Finding the right code in a large repository
Relevant code is hard to locate when an agent does not know its name or where it lives, and every wrong guess is paid for out of the budget the work needs.
What the platform does
Agents receive the project context a change needs, sized to what they can use.
The shape of a project before a file is opened
An agent starts with what is declared where, so a change is reasoned about in the context of the project it lands in.
- Declarations are indexed across the languages we work in
- An agent can see the shape of a file it has not read
- A project map stays readable however large the repository is
Found by name or by description
An agent can search by an identifier or by a description of the code it needs and inspect what comes back.
- Search by the exact identifier
- Search by a description of what the code does
- Results presented for the agent to assess
What a change will touch
Alongside definitions, the engine knows where names are used and which files depend on which. That turns a lookup into a question about consequences.
- What depends on this is answered beside where this is defined
- What a file pulls in, and what pulls the file in
- What changed lately comes back with the answer
Bounded answers, and no customer code leaves the machine
Two rules shaped the delivery side. An answer has to fit the space the caller has for it, and none of the code should have to leave the machine to be understood.
- A broad query cannot flood the agent's context
- The index lives on the developer's own machine and no code is uploaded anywhere
- The project context is refreshed as work changes
What changed in our work
Agents can inspect relevant project context before reviewing or changing code.
Whole repository
What a review starts from
A review or a refactor begins with the shape of the project: what the change touches, what uses it, and what pulls it in. The open file stopped being the boundary of what could be reasoned about.
Budget on the work
Where the reasoning goes
Answers sized to fit mean less of the available context is spent opening files that turn out not to matter. The gain is in what never has to be read.
Stays on the machine
Why we can point it at client code
The index is kept on the developer's own machine and no code is uploaded anywhere.
Do your tools see the whole codebase, or only the open file?
Tell us how your team works with a large repository today and where the context runs out. We will tell you what is worth automating and what is not.
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