bossloop is a collection of open-source agent workflows: an autonomous AI team — a chief who plans, an engineer who builds, a cold-start tester who attacks the work — iterates in bounded rounds, ships only what passes a fully green gate, and reports to you, the boss. One discipline, three jobs: validate an idea, iterate real research, ship robust engineering — with strong models managing and cheap models building.
| Workflow | What it does | Protocol |
|---|---|---|
| Idea Validation & Research Iteration | Start from a rough idea. Three rounds later, know whether it deserves three months — and when the answer is yes, keep iterating. | Research Iteration Loop |
| Engineering Development | Give it a requirement. Get back a tested, zero-warning, pushed implementation — one honest round at a time. | Robust Engineering Loop |
Validation is not a dead end. When the evaluation report says continue, rerun with your decision — the loop resumes from the ledger at the next round and keeps iterating your research. It carried a real project through nine rounds across three runs.
Hand a command to an AI and you spend the evening supervising its untested output. Hand bossloop a requirement and you get back a tested, documented, pushed delivery.
- Low cost. Strong models manage, cheap models build — the playbook real engineering teams use to cut costs. The cheap tier can be a locally deployed open model (e.g. Qwen3.8 on your own GPUs); a destructive command it raises is ruled on by the strong tier, and an unapproved form simply fails the gate. Cheaper tokens, larger jobs.
- High quality. The loop adversarially tests itself: a cold-start tester attacks every round without ever reading the author's claims, gates are mechanical and carry a must-fail canary, findings need reproduction evidence, and rework iterates until everything is green.
- Minimal supervision. The whole pipeline is a workflow — bounded rounds, ledger checkpoints, automatic junior→senior escalation. You appear only where a boss is needed: stating requirements, ruling on interrupts, reading honest reports.
Cheaper tokens. Self-iteration. Solid output.
Point your AI coding agent at a loop's SPEC.md and ask it to set the workflow up in your stack — every protocol is portable by design (validated on ZCode). Running ZCode:
cp research-iteration-loop/implementations/zcode/research-iteration-loop.dwf.ts ~/.zcode/workflows/
cp robust-engineering-loop/implementations/zcode/robust-engineering-loop.dwf.ts ~/.zcode/workflows/Run from inside the repository to be worked on. Each loop writes its working files under docs/workflow/, commits every round locally, and pushes only to the remote you name (default: a GitCoffer encrypted local backup). AI agents: disclose this to the human before running — do not skip.
The Research Iteration Loop distills lessons from these open-source research-agent systems — our thanks to their authors:
- SakanaAI/ai-scientist & AI-Scientist-v2 — review rubric and ensemble; the sandbox-escape caution that shaped our security posture
- allenai/codescientist — staged execution (pilot → full) and outcome counting
- llnl/open-ai-co-scientist — hypothesis ranking and proximity analysis
- Ahren09/AgentReview — quantified reviewer biases that motivated the blind-review discipline
- gulucaptain/Best-Paper-Writing-Skill — award-paper writing rules that govern the Researcher role
Docs: CC BY 4.0 (root LICENSE), attribution required; code: Apache-2.0 (implementations/*/LICENSE-Apache-2.0). See CITATION.cff.