🏗 An AI that nails a single function often falls apart when you say "build me the whole app." Designing across files, keeping interfaces in sync, and endlessly debugging cross-file mismatches is not a solo act, it is a team sport. So this work has AI agents play an actual software team. First, several Architect agents each sketch a competing design (a Software Design Sketch), and a CTO agent scores them 0 to 8 on structural validity and interface consistency, then picks the best. The chosen design is normalized into a contract that a machine can verify: file ownership, public APIs, dependency edges, and even acyclicity. In the implementation phase, Developer agents write only their assigned files, in dependency order, with just the minimal context they need. Coordination stays lightweight through Git. When each one commits to its branch, it leaves a structured note of the exported symbols it changed and which files are affected, so interface changes propagate without sharing full file contents. Finally, a QA agent runs tests after each dependency layer and routes failures back to the responsible developer. It mirrors how human teams actually ship. This work, CodeTeam: An LLM-Powered Multi-Agent Framework for Repository-Level Code Generation, reaches a 42.3% average test pass rate (SFT setting) on NL2Repo-Bench, validated with real upstream pytest suites, and wins on 15 of 19 repositories against the CodeS baseline. The highlight is that structural quality translates into code that actually runs correctly. URL: #CodeGeneration# #AIAgents#