Launching today
agents-cli
The CLI your coding agent uses to ship agents
18 followers
The CLI your coding agent uses to ship agents
18 followers
One command-line tool to scaffold, evaluate, and deploy AI agents on Google Cloud — built to be driven by your coding agent (e.g Antigravity, Claude Code, Codex). Scaffold a production-ready project, evaluate against a real signal, and ship to Agent Runtime, Cloud Run, or GKE or anywhere else!
Agent Starter Pack
the headless self-optimize loop is the part I'd want to understand before trusting it. if the coding agent is scoring its own agent against eval criteria and iterating unsupervised until the number goes up, what stops it from overfitting to the eval dataset or gaming the specific metric instead of actually improving behavior on real traffic? that's a known failure mode any time the thing being optimized also controls the optimization loop. do you have guardrails around eval set size/diversity or a human checkpoint before it ships to Cloud Run/GKE, or is it fully autonomous end to end
Agent Starter Pack
@galdayan Absolutely and great point!
Most of the guardrails live in the skills we load into the coding agent:
- You are always in control of the evaluation process, you can define how the dataset looks like and how varied it is. Coding agents and skills are there to support you with those best practices you mentioned.
- It never deploys without you saying yes. It runs the evals, asks "ok to deploy?", and waits.
Agreed that this won't save you from overfitting a tiny eval set, so it also nudges you to grow coverage once a case passes instead of tuning against a handful of examples.
The 'driven by your coding agent' framing is the interesting bet here. When we let a coding agent run our deploy CLI unattended, the thing that bit us wasn't the deploy logic, it was output format. The agent would read a human-formatted stderr, miss that the deploy half-failed, and cheerfully report success. Are the CLI's outputs, eval scores, deploy status, errors, structured for a model to consume, like JSON with explicit exit states, or the same prose a person reads? That one choice decided whether our agent could actually close the loop.
Agent Starter Pack
@dipankar_sarkar For sure, we are actually continuously improving the output generated by the CLI to be Agent friendly. It's actually a core philosophy of the product: being a CLI optimized for agents first and allow human-friendly output when running in interactive mode
Finally found one 😍