Any autonomous system is really two loops. The work loop does the actual job — designing experiments, running a business, generating a product. The orchestration loop keeps a fleet of agents and tools safe, observable, auditable, and cost-bounded around the clock. I've built a production-grade orchestration loop. It already runs unattended, and it generalizes to wherever your work loop lives.
Every unit of work flows through the same governed spine. Agents do the work, a hard gate stands between execution and production, and a nightly self-improvement loop feeds what it learns back to the start.
A production agent platform on dedicated infrastructure: roughly 30 engines, 5 concurrent learning loops, a curated model-routing gateway, a self-healing escalation bridge, and a nightly "Dream Cycle" that reviews the day and proposes its own improvements. It's designed so a human works about 10 hours a week while the agents carry the load.
Governed at scale: 259 automation surfaces with 99.6% kill-switch coverage, an enforced $250/mo cost cap across 49 model routes, and a real platform migration (OpenClaw → Hermes) executed with a SHA-verified rollback and post-migration soak validation. This is SRE-grade change management, not a notebook.
CUSUM and a windowed z-test from industrial process control, with auto-rollback — the right tool for instruments and models that drift over long campaigns.
Failures become teach-back lessons; the same class stops firing. Unattended recovery, not just alerting.
12-dimension behavioral stability index; content-type-specific hallucination thresholds.
Thompson sampling with a Wilson lower-bound confidence gate before any model can dominate.
TTL-promoted tiers, write-time summarization, archive-not-delete, and a no-silent-drop guarantee on close.
Eight scheduled phases that consolidate, recombine, score, and propose improvements — failure-isolated units.
Spreads fire times to prevent top-of-hour contention across the agent fleet.
Every task carries its AI cost vs. a role-matched human baseline, Bayesian-calibrated per job type.
The orchestration loop doesn't care what the work loop does. It's running today across three very different work loops.
Closed-loop experiment design needs scheduling, drift detection, provenance, and cost governance wrapped around it. That scaffolding is exactly what this OS provides — mapped in detail below.
The same backbone applied to a working company: routing client interactions, wiring tools together, and gating every action with a hard cost cap.
Anywhere you have agents that must run around the clock and stay trustworthy, this is the layer that makes that safe.
Keep the robotics running, schedule around blocked steps, catch drift over long campaigns, and keep every autonomous action auditable and cost-bounded. That layer is what this OS already runs in production — the scaffolding that wraps an experiment-design module, not a replacement for it.
I own the orchestration and reliability layer: multi-agent scheduling, drift/SPC monitoring, unattended recovery, provenance and audit trails, cost governance, and the safety-gating of software actions. I'd partner on the science: closed-loop Bayesian / active-learning experiment design (e.g. BoTorch / Ax, DFT-informed priors), instrument integration (SiLA 2, OPC-UA, lab drivers), and physical safety interlocks — thermal, atmospheric, collision — which are a hardware-authority domain I'd integrate with, never replace. Knowing that edge precisely is the point.
Each pattern below runs in production, and each is set against the closest published work with the specific distinction noted. Search it, filter it, open any card to see the prior art and where it lives.
Distinction: industrial process-control SPC, calibrated for agentic cadence, with auto-rollback — the natural tool for instruments and models that drift over long campaigns.
Closest prior art: CUSUM is mature in manufacturing QC; rarely applied to LLM-agent performance.
Distinction: escalation count is an explicit success metric that must trend down; every resolved failure teaches the system so the class stops recurring. The goal is "never fires again."
Closest prior art: alert systems resolve alerts; few target alert-class elimination as a KPI.
Distinction: every task's AI cost is compared to a role-matched human baseline and Bayesian-calibrated per job type, so high-variance work doesn't hide inside an average.
Closest prior art: cost trackers and t-shirt sizing exist; rarely combined into a live per-task metric.
The full register — 33 patterns, searchable:
No agent touches production on vibes. Every unit of work passes through a contract, a quality panel, and a hard gate — enforced at the data layer, not by reminder.
Role / constraints / tools / workflow / hard-stops, re-read verbatim before every run.
No task is claimable until done, acceptance, and validation are populated. Enforced by the query.
A structured brief, then independent expert-lens verdicts before any production write.
A fresh expert panel re-verifies work before it is ever surfaced to a human.
Software-write gate, kill-switch manifest, and a literal rollback command on every change.
14 named traits — disciplined, precise, honest, follow-through — inherited by every agent and checked in QA. Consistency without fine-tuning.
Escalation count is tracked as a number that must trend down; every resolved failure teaches the system so the class stops recurring.
I'd welcome the chance to compare notes — where the orchestration, reliability, and observability layers are headed, and where these patterns could accelerate the build.