AnaROS is one of Anavec's two products. It operates the heterogeneous AnaRack — networking, CPU, multi-generation GPUs, memory, storage — as one governed system for guaranteed workload outcomes, and drops into your existing brownfield rack when the substrate isn't AnaRack. Two surfaces on one backend: Telemetry (visibility) sees what the pipeline is doing; Behavior (governance + guardrails) constrains what it — and the agents on it — are allowed to do. The Pipeline Governor is the AI-native decision engine that turns telemetry into action. Self-driving operation closes the loop — Diag → Recommend → Simulate → HITL → Act → Verify → Learn — on your rack. Air-gapped capable. Brownfield-ready.
A single AI call traverses your application code → runtime → GPU → fabric → storage in milliseconds. When p99 collapses, the symptom at the top — a slow agent, a stuck retrieval, a timeout — usually has its root cause four layers down: a NIC buffer overflow, a NUMA miss, a noisy neighbor in another tenant. The dashboards you have today don't connect: K8s knows the pods, CUDA knows the kernels, the network team owns the spine — none of them see across boundaries. Business impact: minutes of service recovery become hours of downtime chasing the wrong layer.
Four cooperating layers — L4 workflow, L3 pipeline X-ray, L2 logical fabric, L1 physical rack — visible from one operator surface. Trace any workflow top-to-bottom in a single pane, and the symptom and the root cause finally show up on the same screen. Scroll to drill into each layer.
Agentic flows, MoE applications, inspection pipelines — expressed as a controlled workflow graph with steps, branches, loops, and SLOs. This is the surface CIOs and product owners reason about.
AnaROS decomposes each workflow into Ingest, Preparation, Execution, and Persistence across network, compute, memory and storage — and routes each stage to the right fit GPU class that holds its SLO. Heavy CNN/ViT inference doesn't mean every stage needs an H200.
The Anavec logical fabric resolves source/destination, bandwidth, and zero-copy paths between GPUs, the CPU sled, the staging tier, and storage. The resource map tells you what's allocated, what's draining, and what's free — at all times.
Standard rack envelope. Off-the-shelf modules. Programmable PCIe fabric. CPU and GPU refresh on independent clocks. Pipeline contracts resolve every pipeline edge to a physical switch port through live stage telemetry.
This workflow lives on anavec1(GPU) for ingress and preparation, hops across fabric to anavec2(GPU) for GPU execution, then hops back to anavec1 for storage. Pipeline X-Ray sees all of it — intra-node IPC, inter-node fabric, leaf switches, every stage.
Ingress and Preparation both live on anavec1 — but they're separate processes in separate containers, talking through a kernel pipe. Most observability stacks lose visibility here. The X-Ray traces it: pipe-buffer occupancy, context switches, container CPU contention.
Preparation on anavec1 hands off to Execution on anavec2 — across a real fabric edge. Without pipeline-over-fabric correlation, this hop goes dark. With stage telemetry pinned to fabric identity, the X-Ray lands on a specific switch port pair on leaf1-0 and surfaces drift, loss, and ECMP right there.
Every pipeline edge reconciled with its physical path through leaf/spine. ECMP divergence becomes legible. Ask "where does this workflow live on this fabric?" — and get a topology answer, not a Grafana stack.
Six telemetry domains (Node, Kernel, Process, Net L2/L3, TCP, HTTP/L7, gRPC/H2) crossed with six collector layers (SDI/SSH, Prometheus, cAdvisor, eBPF/Beyla, SDK Py/C++, OTel/OTLP). Each phase adds signals — and unlocks new RateGate capabilities.
Open the switch and you see stage-telemetry signals reconciled to the fabric: resolved endpoints, reconstructions, drifts, violations, latest tick. Verdicts and pending mitigations live next to the switch they apply to, not in a separate ticketing system.
In this example, GPU SM 1% looks like a GPU problem, but the X-Ray correlates intra-node, inter-node, and fabric signals across all stages — and lands on Persistence (suspicion 98%). Storage IOPS contention back-pressures execution upstream all the way to ingress.
| PIPELINE | SRC → DST | HOPS | CLASS |
|---|---|---|---|
| embedding-memtier-pipeline | anavec3 → anavec2 | leaf2-0 | scale-out |
| embedding-baseline | anavec3 → anavec2 | leaf2-0 | scale-out |
| langgraph-semantic-moe | anavec3 → anavec1 | leaf2-0 → spine1-0 → leaf1-0 | scale-up |
| langgraph-semantic-moe | anavec1 → anavec2 | leaf1-0 → spine1-0 → leaf2-0 | scale-up |
| b10-resnet-classify | anavec1 → anavec2 | leaf1-0 → spine2-0 → leaf2-0 | scale-up |
| PIPELINE | EDGE | PORTS | BOUND |
|---|---|---|---|
| langgraph-semantic-moe | preparation → execution | leaf1-0:Eth80 → leaf2-0:Eth80 | ✓ bound |
| langgraph-semantic-moe | post_proc → persistence | leaf2-0:Eth88 → leaf1-0:Eth80 | ✓ bound |
The Placement Engine doesn't guess. It queries a canonical, second-by-second model of the rack on every decision — the same source of truth that powers telemetry, chargeback, and audit.
MCP / LLM /API gateways sit with SecOps. They own the conversation — prompt policy, tool-call scope, output filters.
The moment an agent SSHes a switch, pushes config, or shifts traffic — static rules (ACLs, segments) can't reason about intent, scope, or consequence. AnaROS provides Infra teams with the governance + visibility for the unguarded surface.
Every pipeline declares its operating contract once — SLA, path, policy. Enforced everywhere.
Where each stage runs. Whether the next workload admits. Deterministic math against the live model.
Every rack, drawer, link, pipeline, model — one live picture, provenance-stamped, sub-minute cadence.
Per-stage instrumentation across CPU, GPU, fabric, storage. Attributes p99 to where the latency was born.
Every action tiered before it executes. Destructive ones gated for approval. Rollback ready.
From workflow to silicon — vertically across layers, horizontally across the pipeline journey.
Placement, admission, and pipeline governance — maximize utilization, minimize CAPEX.
Predict what an action does to the live fabric — before it does it. Tier, scope, blast-radius gate.
Vertical visibility → Horizontal visibility → Workload placement + governance → Infrastructure guardrails — all powered by the same five engines and one canonical state. The same AnaROS console runs on your homelab today.
