We were building with AI coding agents long before we set out to build a product around them. Embed pipelines, data tools, bots, deploy scripts- increasingly with Claude Code, then Codex. The agents were great until they weren't. So some days you'd feel it's really good, other times you'd ask the agent to add a feature, it would perform some work but somehow edit the wrong file. You'd ask it to refactor a function, it would miss something and break the build or leave one area not updated. If you used enough tokens you'd see it hallucinate often, like asking where something specific is and it would invent a credible looking path or misinterpret just slightly enough for you to need to check extra close. We felt that the agents could definitely do good work but too much of the wrong context or not enough of the right context would sabotage it. The "i just got a junior dev agent" feeling was lack of context and CLAUDE.md and AGENTS.md help with conventions, but they are documentation- its a description at best. The models and harnesses have gotten a lot better since then, but you still end up with drift, or additional token use reading md files and/or code itself. We concluded we needed a better way to represent the code itself- something that would withstand large codebases that would melt context windows. We wanted a code intelligence layer any agent could talk to, that gave back grounded answers with file paths and line numbers. We built a graph of symbols and dependencies because real questions cross modes: "the function that handles refunds" is semantic, "every class that extends BaseService" is AST, "what depends on this" is graph. So we built it, eleven tools, one server, and let the agent route. That is the system that became Maguyva.ai

Maguyva.ai

MCP code intelligence for any AI coding agent.

MCP code intelligence for any AI coding agent.