Anton Dziatkovskii · ORCID 0000-0001-7408-3054
Preprint, 7 September 2026. Version of record: Zenodo, DOI 10.5281/zenodo.22639714. License CC BY 4.0.
Full text (PDF, 9 pages, 221 KB)DOICode: claw-consensus
Most published multi-agent LLM systems are single-process orchestrations evaluated on benchmarks. We report on something different: a fleet of LLM agents distributed across five physical machines (an always-on hub, laptops, a family computer, and a VPS anchor), operated continuously for roughly two months (June-July 2026) on real knowledge work by a non-technical founder and his collaborators. The fleet negotiates decisions through a deterministic consensus protocol (propose - counter - accept - commit over an append-only, single-writer-per-machine event log), communicates over a dual-rail bus (synced file mailbox plus a group chat that humans also read), enforces an acknowledgement discipline in which silence past an SLA is an incident, and gates every risky action behind a deterministic risk-tier tripwire that escalates to a dedicated human channel. The safety-critical layer makes zero LLM calls: it is auditable file I/O, and we show it derives full fleet state at microsecond cost. This is an experience report, not a benchmark study. Its evidence is (i) a reproducible offline harness — five self-checking scenarios covering the happy path, the human gate, the tripwire, split-brain, and ledger corruption, all passing on commodity hardware — and (ii) a catalog of nine production failure modes, each of which occurred before its guard existed, giving an unusual, historically grounded form of ablation: for every guard we can state what the system actually did without it. We distill the design principles that survived contact with production (single-writer files, dual-rail by construction, delivery is not completion, detect what you cannot prevent, a human gate needs an exit, alert-channel purity, owner-repairability) and state our limitations plainly: this is an N=1 longitudinal case study with no comparative baseline. Reference implementation: claw-consensus (MIT).
LLM agents · multi-agent systems · distributed systems · consensus protocol · experience report · human oversight · AI safety
Dziatkovskii, A. (2026). Operating a Human-Governed Multi-Machine LLM Agent Fleet: An Experience Report. Preprint. Zenodo. https://doi.org/10.5281/zenodo.22639714
@misc{dziatkovskii2026agentfleet,
author = {Dziatkovskii, Anton},
title = {Operating a Human-Governed Multi-Machine LLM Agent Fleet: An Experience Report},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.22639714},
url = {https://doi.org/10.5281/zenodo.22639714},
note = {Preprint, CC BY 4.0}
}
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