Agents are good at writing code. They're far worse at navigating the system around it: the related code, the pipelines that run it, the deployments that ship it, the work items that asked for it, and the teams that own it. That gap is where AI-assisted engineering breaks down today.In a large monorepo, the gap shows up as wasted iterations, blown token budgets, and code that looks correct but gets reverted. Across repos, it's worse: the context window fills before the agent finds the answer, and the task fails outright. Teams end up spending more time fixing agent output than the agent saved them.GitLab Orbit, now in public beta, closes the gap. It's a live, queryable graph of all your code, merge requests, pipelines, deployments, vulnerabilities, and ownership, with every relationship between them kept current as your team works. Agents reason from first-party GitLab data instead of stitched-together tool calls. Engineers can query the same graph through the Data Explorer to trace changes, investigate incidents, and answer the cross-system questions that today take hours of manual reconstruction.Proven on real merge requests at Compare the MarketCompare the Market, a U.K. price comparison platform, tested four context retrieval approaches for an internal AI code reviewer across 79 real merge requests. The Orbit-grounded reviewer placed accurate inline comments about 70% of the time, against about 58% for retrieval-augmented generation (RAG), and captured more of the key changes in summaries (68% vs. 66%). RAG underperformed every other approach, including no context at all."Orbit gave us an AI code reviewer that actually understands our codebase, not just the diff in front of it. We tested it against RAG and a few other approaches across real merge requests, and the gap was clear. Better comment placement, better summaries of what actually changed. RAG, which we'd assumed would be the natural solution, ended up performing worse than no context at all. For us, that result spoke for itself."