Without context and traceability, a coding agent becomes a bottleneck

WHAT HAPPENED

They created a Git built for AI agents

The git-ai repository describes itself as “Git designed for AI agents”. The idea is to replace the classic flow, built for humans, with a server-centric layer for many agents reading, writing, and delivering code at the same time.

THE NUMBER

50

agents in the project's benchmark

In the test described in the source, there were 50 agents and 250 changes in one branch.

THE REAL PROBLEM IN COMPANIES

What I see in companies when agents enter the codebase

1. 01Each agent produces something, but without shared context the handoff breaks
2. 02Review becomes loose, with no clear trail of who changed what and why
3. 03The human becomes AI tech support instead of making business decisions

I HAVE ALREADY SOLVED THIS

In the AskMoses.AI development team

1. 01100% agentic development team
2. 02Each dev works with Claude Code on the project
3. 03Another Claude reviews the pull request

MY TAKE

Simple that works is worth more than complex that impresses.

In practice, a coding agent without an operations layer only increases the volume of change. What sustains production is context, review, handoff, and rollback. If that is clear, AI helps. If not, it only speeds up the chaos.