Campus is a shared project workspace built for teams and AI agents to work together without losing context. Instead of scattering work across chat threads, documents, tickets, canvases, and separate AI tools, Campus keeps your repository, terminal, project knowledge, conversations, and AI agent work all in one persistent place, organized around each project.
Introduction
Modern software teams juggle a growing number of tools: a code editor, a terminal, a ticketing system, a docs wiki, a chat app, and now one or more AI coding assistants or agents. Each of these tools holds a piece of the picture, but none of them holds the whole picture. When a human teammate hands off a task, or when an AI agent picks up work that was left unfinished, critical context is often lost. Campus was built to solve exactly this problem by giving every project a single, persistent workspace that both people and AI agents can read from and write to.
Key Features
At the center of Campus is the project workspace itself, which brings together the repository, a live terminal, project knowledge, and ongoing conversations in one continuous environment. Because everything lives in the same place, there is no need to re-explain the state of a project every time a new session starts, whether that session is run by a human or by an AI agent. Conversations and decisions made during a task stay attached to the project, so anyone (or any agent) coming back to it later can see exactly what happened and why. This persistent memory means unfinished tasks can be picked up again without having to reconstruct context from scattered notes or old chat logs.
Use Cases
Campus is well suited to engineering and product teams that increasingly rely on AI agents alongside human contributors. A developer can start a task, hand it off to an AI agent to continue overnight, and return the next day to find the work, the reasoning behind it, and the relevant files all still connected in the same workspace. Teams that currently spread project context across a repository, a separate ticketing tool, a wiki, and multiple AI chat windows can consolidate that context into a single shared home. This is especially useful for teams running multiple AI-assisted workflows in parallel, where keeping track of which agent did what, and why, quickly becomes difficult without a shared system of record.
Benefits
By unifying the repository, terminal, project knowledge, and conversations into one workspace, Campus reduces the overhead of context switching between tools. It helps teams avoid the common problem of important information being buried in a chat thread that nobody can find later, or a decision being made in one tool that never makes it into the documentation. Because both humans and AI agents operate against the same persistent project context, collaboration between the two becomes more direct: agents can build on prior work instead of starting from scratch, and humans can review and continue agent work without needing a separate debrief.
Conclusion
Campus is designed for a world where AI agents are becoming regular participants in software projects, not just occasional assistants. By giving every project a shared, persistent workspace for code, terminal access, knowledge, and conversation, Campus helps teams and their AI agents stay aligned, avoid duplicated effort, and keep project context in one place instead of scattered across many disconnected tools.
