AI models get smarter every day. What they do not have is the context of your network. Without it, an agent is a brilliant new starter on its first day, every day.
The context layer turns your knowledge, expertise and processes into context a machine can use.
Knowledge
What exists. Sites, devices, circuits, and what depends on what.
Expertise
How the network is managed. What your engineers learned from outages and failed changes.
Processes
Which actions are acceptable, and who has to approve them.
Run like software: owned, approved, versioned and kept current.
We get there in four steps
01 / 04
Years of experience become playbooks.
Your best engineer knows which sites are critical and who to call, without looking anything up. That came from shadowing colleagues, living through outages and rolling back failed changes. We write it down.
The agent earns autonomy. You grant it.Every action has evidence, a named approver and a change record.
Collect diagnosticsLow riskOn its own
Clear a stuck portLow riskOn its own
Fail over a siteHigh riskWith approval
How an agent works.
The agent reads from your toolsInvestigating
Your tools
Service desk
Monitoring
Controller
DDI
Devices in scope
Read-onlyEngineer approval
Agent
It does the legwork.
The agent reads alarms, tickets and telemetry from the tools you already run. It checks device state, recent changes and past records at the same time.
An agent with access to production needs tighter rules than a new engineer. These apply to every agent in the catalogue.
Read-only until you say otherwise
Every agent starts by observing and recommending. You choose which actions it may take.
A person approves every change
Nothing that alters production happens without a named engineer approving it.
Evidence with every answer
Each diagnosis shows the data it came from, so your team can check it in seconds.
Your tools stay in charge
Agents work through your service desk, monitoring and controllers. Records stay where they are.
Everything we build lives in your environment, and you own it.
PlaybooksSite consoleContext modelWorkflows
Use cases.
Change agent
Change request risk review
People are good at judgement. Machines are good at relentless diligence. The agent applies the same context to every scheduled change and spots risk before the change window opens.
$ review change-request
plan ......... complete
rollback ..... not tested
clash ........ same site, same window
verdict: send back with feedback
Release agent
Vulnerability to upgrade plan
Maps a new security advisory to the devices it affects and proposes a staged rollout.
$ assess advisory
affected ..... listed by site
target ....... approved version
rollout ...... waves, pilot site first
verdict: plan ready for approval
Incident agent
WAN circuit outage triage
Groups the alarms from one outage and opens a ticket with the diagnosis in it.
$ triage alarm
alarms ....... grouped into one incident
backup ....... carrying traffic
cause ........ carrier fault
next: raise carrier case (needs approval)