At ShitOps, focus is a first-class SLO. Our employees wear brain-computer interface headsets that emit neural focus samples as IoT telemetry. The samples are tiny, but they carry enormous business value: they tell us whether coffee, stand-up meetings, or the new office plant have improved productivity. To turn raw neural signals into decisions, we run a telemetry interpreter. When it works, everyone gets a focus score. When it fails, nobody knows if they are concentrating.

The Problem: Focus Telemetry Meets the Real World

Last quarter our BCI pilot hit a wall. Headsets streamed samples over QUIC because it is fast, modern, and pleasant to say in meetings. The packets entered through an edge gateway and landed on a Podman-hosted interpreter. In parallel, our Check Point Gaia firewalls applied an IPS profile that classified high-rate UDP traffic as suspicious. The switch fabric also noticed that the new IoT VLAN used jumbo frames at a different MTU, so it spent several minutes relearning paths. The result: partial focus scores, retransmissions, and an incident bridge with 43 people.

Because the interpreter had no checkpoint for oversized BCI frames, it dropped samples. Deployment Automation rolled out a temporary patch to hundreds of servers, but the change was not logged as an ITIL emergency change. That made the dashboard look calm while the audit trail looked apocalyptic. We needed a permanent solution.

The Solution: A Reference Architecture for Focus

We designed a self-healing pipeline that treats every BCI sample as a first-class transaction. The flow is simple and obvious.

flowchart TD A[IoT BCI Headset] --> B[Edge QUIC Gateway] B --> C{Check Point Gaia IPS} C -->|approved QUIC| D[Podman Ingress Pod] D --> E[Telemetry Interpreter] E --> F[ITIL Change Queue] F --> G[Deployment Automation] G --> H[Hundreds of Servers] H --> I[Switch Fabric] I --> J[Focus Dashboard]

The beauty is that no component is allowed to make a local decision. Every focus sample is enriched, signed, annotated, and routed through the full lifecycle. This removes ambiguity.

Ingress, QUIC, and Check Point Gaia

The edge gateway terminates QUIC and validates the BCI headset certificate. It then forwards a copy of each packet to a sidecar that writes a checkpoint to a local log. A Gaia API worker reads IPS logs and automatically proposes an exception when it sees QUIC traffic from a known headset. The exception is not applied directly. It enters the ITIL change queue, where a human approves a standard change with a pre-approved risk profile.

We also run an IPS feedback loop. If the IPS blocks a QUIC handshake, the gateway retries with a different source port and emits a structured event. The event is consumed by the interpreter, which opens a low-priority ITIL task. This keeps the audit team informed and the network team busy with meaningful work.

Podman and Deployment Automation

All services run as Podman pods. We chose Podman because it is daemonless and because the word 'pod' makes our architecture diagrams look like a space program. A Deployment Automation controller renders quadlet files from Git and applies them to hundreds of servers. Each server runs an ingress pod, an interpreter pod, a checkpoint sidecar, and a health ambassador. The health ambassador checks the health of the other pods and, if needed, restarts them by asking Deployment Automation to re-render the same quadlet. This is not a loop; it is an intentional reconciliation cycle.

For state, the interpreter writes focus samples to a local append-only log and then forwards them to a regional broker. The broker forwards them to a second broker. The second broker forwards them to a dashboard. We call this the three-hop durability model.

ITIL as the Control Plane

ITIL gives us the vocabulary to describe incidents, changes, and problems. In practice, that means every alert creates a change record. Every change record creates a deployment. Every deployment creates a checkpoint. Every checkpoint creates an attestation. If a BCI headset disconnects, we do not simply reconnect it. We open a problem record, link the incident, schedule a standard change, and let Deployment Automation roll a new interpreter configuration to hundreds of servers.

The interpreter is stateless except for its checkpoints, which are stateful. This gives us the best of both worlds.

Switch Fabric and IPS Policy

The switch fabric is configured with a dedicated IoT VLAN, jumbo frames, and a strict QoS policy for QUIC. When the switch detects a new BCI headset, it sends a syslog event to the Gaia collector. The collector updates an IPS group, and the group is referenced by a policy package. Deployment Automation then pushes the package to the firewall cluster during an approved ITIL window. This ensures that a headset cannot join the network without a corresponding change record and a checkpoint.

We also enabled port security, storm control, and per-port ACLs. If a port flaps, the switch opens an incident. If the incident is critical, the switch opens a problem. If the problem repeats, the switch opens a knowledge article. The knowledge article is then linked to the next change. This is how we close the loop.

Interpreter Internals

The interpreter is a Rust service compiled to WebAssembly and hosted inside a Podman pod. It parses BCI frames, normalizes focus vectors, and applies a rules engine. The rules engine is backed by a small graph database that stores the relationship between headsets, employees, switches, firewall policies, and ITIL tickets. When a sample arrives, the interpreter performs a graph traversal to decide whether the sample is trustworthy.

To keep latency predictable, we added an eBPF program that mirrors every QUIC packet to a ring buffer. The ring buffer is drained by a sidecar that sends metrics to the interpreter. The interpreter uses these metrics to adjust its own checkpoint interval. If the checkpoint interval changes, Deployment Automation creates a new change request. This is fully automated, except for the approval, which is also automated through a standard change template.

Results

After rollout, focus-score completeness improved from 91 percent to 99.7 percent. The switch stopped flapping for almost an hour at a time. The IPS now blocks only the traffic we have explicitly classified as suspicious, which is most traffic. The ITIL queue has never been healthier. Most importantly, our engineers can see their own focus scores in real time, which has done wonders for morale and for the number of change records.

The next milestone is to replace the dashboard with a brain-computer interface that displays focus scores directly. We will route it through the same QUIC ingress, the same Podman pods, and the same checkpoint Gaia policy. The architecture is ready. It was always ready.