The autonomous research team for production ML. You define the oracle on your data — Zero Operators delivers an audit-ready trained model your domain expert will sign off on. Built by specialised agents that cross-check each other.
Most AI tools help you write code. Zero Operators delivers a trained model — and the recipe to reproduce it.
You write a plan.md with your data, your metrics, your tiered success criteria. A lead orchestrator decomposes it. Specialist agents — data engineer, model builder, oracle, domain evaluator, code reviewer — run the work in parallel and check each other's output. Memory persists across sessions, so the same mistake can't happen twice.
Your part is the research direction, not the typing. You approve the plan, review the gates, read the final report. Out the other end: a checkpoint, a recipe, an audit log — everything a domain expert needs to sign off on the work.
The contract is small on your end and large on ours. Everything between is autonomous.
One generalist agent judges its own work. Zero Operators is a team — five specialised roles working in parallel against a shared task list. Different cognition checks the work. The lead assigns. Peers communicate directly. Nobody marks their own homework.
Lead spawns a shared task list. Five specialised agents claim and complete tasks in parallel — and talk directly to each other to coordinate. Cross-checking is built into the topology, not asked of one model.
Run zo build and this is what happens — a real tmux session, the lead on the left, a spawned research-scout on the right.
A real tmux session — the lead on the left with your chat input, and the spawned agents stacked on the right, each running its own task. The orchestrator decomposes your plan.md, peers report back, and gates only advance when the oracle says so.
Each phase has a contract — inputs, outputs, success criteria, budget. Human gates sit at feature selection and analysis. Everything else runs until the oracle says pass.
Source, validate, version. Nothing leaves this phase unlabelled, unseeded, or un-hashed.
agents · data-engineer · scoutProposals from the feature bench. You approve the set. This is the only place the agents wait on you.
agents · feature-synth · statisticianArchitecture drafted, hyperparameters defined, baselines proposed. Contract spawned for training.
agents · model-builder · architectRun, evaluate, learn, retry. The oracle decides when a model is done — not the model.
agents · trainer · oracle · analystResults, failures, confusion. You read the report. You approve the narrative. You decide if it ships.
agents · analyst · writerClean delivery repo. Zero infrastructure artifacts. A bundle your team can deploy without reading our docs.
agents · packager · release-engNo agent marks its own homework. And no mistake gets to happen twice.
Every phase ends with a verdict. Must-pass gates block delivery. Should-pass flag concerns. Could-pass surface warnings. Your targets, not ours.
Tiered. Deterministic. Your targets become the oracle's contract. No agent marks its own homework.
The entire project state lives in a portable .zo/ directory. Pause here, resume anywhere. Laptop to cloud, GPU rig to CI runner — the context moves with the work.
STATE · DECISION_LOG · PRIORS · semantic recall. Same mistake? Literally cannot happen twice.
Each category operates at a different level of abstraction. Coding assistants work a line at a time. Agent frameworks work a task at a time. Zero Operators ships a verified, audit-ready model — on your data, against your oracle.
# Clone and set up. ❯ git clone https://github.com/SamPlvs/zero-operators.git ❯ cd zero-operators && ./setup.sh # Initialize a project. ❯ zo init my-project ❯ zo draft --project my-project # Build. Walk away. ❯ zo build plans/my-project.md # ⏵ phase 01 · data pass # ⏵ phase 02 · features awaiting gate # ⏵ phase 03 · model pass # ⏵ phase 04 · training pass · oracle ✓ # ⏵ phase 05 · analysis awaiting gate # ⏵ phase 06 · packaging pass # → delivered to ~/deliveries/my-project · all gates PASS▊