Autonomous telemetry control: a reference architecture

Summary

Telemetry infrastructure in most organizations was not designed. It accumulated. Shippers, forwarders, message buses, platform-side parsers, data lakes, and now AI assistants were each added to solve a real problem, and together they form a system in which a change to one source can break rules in three tools and no single component knows where a given event went.

This paper describes a single architectural layer that owns telemetry from the moment it is produced to the moment it is consumed, exposes that ownership to both people and software agents, and treats the ongoing operation of the pipeline as work to be automated under human-defined limits. We call this layer autonomous telemetry control, and CyberAIX is an implementation of it.

1. What the layer does

The layer has six responsibilities.

1. Collect from every source through a uniform interface, without installing software where it can be avoided.
2. Transform in motion: remove noise, apply a canonical schema, attach context, enforce policy.
3. Route to any number of destinations according to declarative rules.
4. Record the path of every event so that its journey from source to destination can be reconstructed.
5. Search across destinations in place, without re-centralizing data.
6. Operate itself, delegating monitoring, drift detection, and repair to autonomous agents within declared limits.

The sixth responsibility is what distinguishes this layer from a conventional pipeline. Earlier products stopped at routing and left the keeping-it-working to a person on call.

2. Components

Connectors. Source and destination adapters. This is the only place vendor-specific knowledge lives.

CyberAIX Pipeline. Collection, reduction, and delivery. Stateless with respect to event content; stateful with respect to delivery guarantees.

CyberAIX Stream Intelligence. Enrichment, policy evaluation, correlation, and action applied while events are moving.

CyberAIX Federated Search. A query layer that uses the pipeline's routing record to fan a single question out across destinations and return a joined answer. It also hosts CyberAIX Gateway, the governed interface through which AI agents read data.

Agents. Autonomous workers that operate the components above: CyberAIX Pilot for pipeline health and repair, CyberAIX Scout for investigation assistance.

CyberAIX Nexus. A relationship store that holds the context the other components attach and query.

Universal collectors and CyberAIX Relay. Deployment forms of the pipeline: lightweight CyberAIX Pipeline universal collectors for remote and operational sites, and CyberAIX Relay as the central shaping and routing engine.

3. Contracts

Three agreements hold the components together.

One schema. Every event leaving the pipeline conforms to a single canonical schema. No downstream component ever sees a vendor-native format.

A routing record. For each event, the pipeline records which destinations received it. Search depends on this record to know where to look.

Policy as configuration. Reduction, enrichment, routing, and agent limits are declared in versioned configuration rather than embedded in code. This is what makes any destination replaceable by editing a file.

4. Properties an implementation must have

Neutrality. No dependency on any particular analytics platform, storage system, or AI model. Removing any destination must require only a configuration change.

Losslessness by default. Reduction is a routing decision. Events removed from a premium destination go to a cheaper one unless policy explicitly discards them.

Self-observation. The layer emits telemetry about itself: volume per source and destination, parse success rate, latency, and drift indicators.

Bounded autonomy. Agents act within declared limits. Repairing a parser is permitted. Changing a routing rule that affects a compliance destination requires a person.

5. Deployment shapes

Central. One cluster in a cloud region receives everything. Suits cloud-native estates.

Universal mesh. Lightweight universal collectors at each site, each running policy pushed from the center and forwarding reduced telemetry inward. Suits operational, retail, and distributed environments.

Hybrid. Universal collectors for on-premises and operational sites, central collection for cloud sources, one policy across both.

6. Adoption sequence

1. Introduce the layer as a pass-through in front of the existing analytics platform.
2. Turn on self-observation and profile every source for two weeks.
3. Apply reduction policy in stages, each previewed against live detections.
4. Move schema normalization out of the analytics platform and into the pipeline.
5. Add secondary destinations: low-cost storage first, then additional tools.
6. Enable in-place search across destinations.
7. Enable agents in observe-only mode, then grant repair permissions progressively.

7. Closing

Telemetry in motion needs an owner. That owner should not be any single destination, and the ongoing work of keeping the data trustworthy is well suited to automation under limits a person sets. That is the whole argument, and the rest is engineering.