AI ON AEX
 

Your engineers are spending their week on diagnostics they've run a hundred times.

AEX is building operational agents for fiber networks, grounded in live network data and real resolution history, with the customer holding the autonomy dial. 

~85% 
of operators lose a fifth or more of the engineering week to manual diagnostics

~25%  
lose more than 60% of that week to the same manual work

50%
picked network troubleshooting as where AI should apply first

 

The real constraint is not capability. It is permission.

A majority of operators polled said they'd allow an agent to diagnose but not to act, and most of the rest wanted human approval before any change. That's the right instinct: a confidently wrong recommendation is a nuisance in a chat window and an outage on a network. So we designed for it rather than around it.

Identity-scoped Execution

Every call runs using the identity and token of the user it acts for. Your existing auth, RBAC, and identity management stay in force. No privileged service account, no second permission surface to audit.

No Free-form Commands

The agent cannot compose arbitrary commands. Actions are constrained to defined action steps, and every change is authorized by a human.

An Autonomy Ladder You Control

You advance autonomy one fault class at a time, on the evidence of that fault class's measured first-pass resolution rate. Not a switch you flip and hope. 

Full Audit Trail

Every diagnostic step, every action, and every authorisation is recorded. 

 

Why this works when a chatbot over an API does not

Every OSS/BSS vendor will claim AI this year, and most will ship a chat window over their existing APIs. General-purpose models fail in live networks in specific, predictable ways: they lose critical detail inside long runbooks, they produce different answers to the same question, they hallucinate actions, and they have never seen the long tail of operator, OEM, and firmware combinations where real incidents live. 

Bounded fault classes

The agent retrieves and reasons over only the fault classes relevant to the event, keeping context small enough for retrieval to stay reliable. Each fault class defines explicit diagnostic steps and branch conditions.

Grounded in real networks

Continuous monitoring of OLTs, switches, BNGs, rectifiers,  batteries, and backhaul. Proactive fault detection, field and third-party coordination, and root-cause analysis and reporting.

Your data stays yours

Run through Microsoft Foundry on Azure, not consumer AI services. Prompts and completions aren't shared with other customers or used to train foundation models. Stored in your designated geography, AES-256 encrypted.

Built on an open standard

Capabilities expressed as use cases served over MCP, the default integration standard for agent tooling.

Where we are now

LIVE NOW Agent AEX: AI-assisted network troubleshooting with measured performance scoreboards per fault class
IN PROGRESS An expanding fault-class library and agents across provisioning, coverage and premise, billing and product, customer and service, and field service.
NEXT Self-healing networking: troubleshooting integrated with monitoring so faults are resolved before they become tickets
Whitepaper · Free download

How to build self-healing networks with AI

Our latest whitepaper shows the architecture, the guardrails and the proof required before anyone should trust autonomy on a live fiber network.

Download the whitepaper
Live today

Agent AEX is the first agent on AEX

Agent AEX handles network troubleshooting. When an issue is detected, it runs a full diagnostic sequence across your network, identifies the root cause and presents a recommended fix for your engineers to approve before anything changes. It works across OEMs, including Calix, Adtran, Nokia and Ciena, so your team works in one workflow whatever hardware is in the ground.

Included for all AEX customers at no extra cost.

See how Agent AEX works

How Agent AEX handles a fault
  1. 1
    DetectAn issue triggers the full diagnostic sequence automatically
  2. 2
    DiagnoseCross-vendor analysis across every OEM in your network
  3. 3
    RecommendRoot cause identified with a clear recommended fix
  4. 4
    ApproveYour engineer approves before any high-impact change
    New
Whitepaper · Free download

Only 18% of network-automation projects are rated a complete success.

The ROI Case for AI-Driven Network Operations breaks down what separates the 18% from everyone else, and what agentic AI returns when it's engineered for a live fiber network.

Download the whitepaper

See the troubleshooting agent work a live fault.

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