Governed Autonomy for GPU Clusters and Networks: from human intent to nanosecond in-ASIC reflexes

An autonomous control plane for infrastructure is not an LLM with kubectl — it is a governed loop where a planner proposes, a separate referee approves against an allow-list, typed tools execute, and every action is verified against a certified contract. This repository is the architecture for that loop across two domains — the AI cluster and the autonomous network — plus its signature evidence: a sourced latency hierarchy showing that autonomy never makes one loop faster; it certifies policy at human timescales and compiles it downward into pre-authorized reflexes.

The through-line: the chaos-fidelity standard certifies that a recovery behavior works; reliability economics prices what it is worth; this repo is the controller that must pass those experiments before it is trusted — and the promotion of any action up the autonomy ladder is machine-checked against exactly that evidence [18].

Fifth in the DIMAGGI series on turning GPU capital into usable compute. All claims trace to REFERENCES.md.

The signature exhibit: the latency hierarchy

"Can infrastructure react in nanoseconds?" is the wrong question — nanosecond decision-making exists nowhere. The right question is how far down the latency hierarchy a governed system can push policy it has certified, and the answer is already sub-microsecond for compiled models (switch-ASIC forwarding, per-packet adaptive routing, in-network P4 inference at <450 ns/packet) while the sub-10 ms RAN tier has no standardized control point at all yet. Every rung is sourced; the figure and table are generated from one data file. Full reasoning: docs/latency-hierarchy.md.

The architecture

Five planes kept separate so a planner cannot edit policy by talking well (intent, decision, world-model + checker, sense, actuation); a referee that is a different identity from the healer ("if one process can propose and approve a drain, you have no control plane"); a cycle that fails closed when the brain is dark; and an autonomy ladder (L0–L4) where an action class is promoted only on the evidence of a green chaos experiment. Details: docs/architecture.md. The same discipline instantiated for DC fabric, IP, optical, and RAN — aligned precisely to TM Forum's AN levels and O-RAN's loop timescales — is in docs/autonomous-networks.md.

The machine-checkable artifact: the promotion gate

Consistent with the standard's "fail the PR, not the prose" ethos, gate/ refuses an autonomy-promotion record that claims a level it has not earned: L2+ must cite a certifying chaos experiment, carry evidence, and hold its abort; L4 needs a control-plane-dark drill and a rollback drill in one declared pool; irreversible fault domains (production training fabric, power interlocks) are capped at L1.

pip install pyyaml matplotlib
make test        # promotion-gate validator + rejection tests
make exhibit     # regenerate the latency-hierarchy figure from its data file

Or install the gate as a command and check your own promotion records:

pip install git+https://github.com/dimaggi-ai/governed-autonomy
promotion-gate my-promotion-record.yaml    # refuse it if the level is unearned

Honest scope

This is an architecture and a sourced latency map, not a cluster or network simulator — the quantitative work lives in the sibling repos. The network chapter is standards-aligned prose evidenced by early public field demonstrations, not a benchmark; where an earlier draft paraphrased TM Forum or O-RAN loosely, the corrected framing is stated inline. The latency hierarchy carries its own qualifications (the nanosecond tier is thin; the sensing floor binds first).

Series — turning GPU capital into usable compute

• GPU Cluster Networking (network-vs-more-gpus)
• GPU Cluster Scheduling (scheduler-vs-more-gpus)
• Chaos Fidelity Standard (ai-cluster-chaos-fidelity) — certifies the experiments
• Reliability Economics (reliability-economics) — prices which recovery policy wins where
• Governed Autonomy (this work) — the controller that must pass them, cluster and network

Margaret (Maggie) Nanyonga — Founder & Principal Architect, DIMAGGI AI. Governed AI infrastructure: the control, reliability, and audit layer for autonomous systems operating production networks and compute.