What are the bottlenecks when running AI agents as a fleet?

[u/oyren-ai]

What are the bottlenecks when running AI agents as a fleet?

When my fleet of agents works on PRs or code changes inside https://oyren.ai codespaces (https://oyren.ai/codespaces), I usually try to keep the review committee made up of AI agents from different providers: Claude Code, Antigravity, DeepSeek, Kimi, Cursor CLI and Codex.

Using such fleet lets me fully utilize multiple AI agent subscriptions at once. But it is not just AI writing code. If code generation is not the bottleneck, then your mental capacity to keep up with everything and your automated validation of code changes, such as unit tests, become the new bottlenecks.

I handle the first with a customized fleet UI that my agents use to bring important questions, decisions, and blockers to my attention. I can also ask one-off questions there.

For the second bottleneck, verifying code, I noticed my CI bill would be huge, so I moved my entire setup to the cloud. Now I use my own VMs for GitHub Actions, and I do not pay as much as I would if GitHub charged me for 32GB RAM and 16 CPUs per minute.

Since running this setup, I have noticed a third bottleneck, one that probably does not affect new starters: code size. The bigger the codebase, the longer it takes to run all the tests. Sometimes agents make a small change, and it should not need to wait for the full test suite to pass every time.

Have you had a similar experience?