Any company who presents their first whiteboard image for their product at an event like AI Field Day #8 is sure to capture my immediate attention.
This was the first time I’d heard from Selector AI at a Tech Field Day event. I found them to be gutsy and independent while offering unique solutions to figuring out network issues in modern AI system architectures.
A Man’s Got To Know His Limits
Some background: I’m a reasonably experienced Oracle DBA, so I know how important a reliable network is for applications to connect to my databases.
I also know my limits.
I know I’d never be able to fill in as a backup network administrator during an irritating recurrence of a periodic latency, and definitely not during an unexpected crisis. I see the value of Selector AI’s platform as insurance against me doing something stupid after I guessed at a solution based five minutes’ research on Stack Overflow.
It’s All Fun & Games … Until Somebody Loses a Packet
Finding the true root cause of a network performance latency often becomes a multi-vendor finger-pointing exercise, especially when up to that point in time everything appeared to be working nominally.
When you factor in the obvious – no two IT shops’ network infrastructure is truly identical, even if they used identical hardware – it can be nearly impossible to assess whether sudden latency is expected because of normal business work schedules, versus the failure of a critical hardware component or because a newbie engineer deployed an untested DNS configuration.
That’s why Selector AI built their monitoring platform with the ability to capture the specific context of their client’s network performance during what would be considered normal and acceptable.
Selector AI’s platform can then deploy bespoke monitoring via proprietary AI models they’ve developed to filter out the noise from millions of log posts produced from many thousands of network devices to isolate root causes effectively.
When A Single-Shot Solution … Isn’t.
Joby Rudolph, Selector AI’s distinguished engineer, demonstrated in detail how the most recent version of their platform came into being.
Their first version used a single-shot AI approach to solving network issues; it enabled an unskilled human to ask questions using a proprietary NLP chatbot approach to ask simple questions about the network’s state. But as more robust AI infrastructure has matured in the past 18 months – especially the capabilities of Model Context Protocol (MCP) tools – Selector built the latest version of their platform around that orientation.
They realized the models already developed for detecting network issues were still valuable, but they migrated their solution from single-shot inferencing to leverage an agentic approach instead.
This latest version deploys three different types of agents to diagnose and solve a non-performant network:
- An orchestrator agent coordinates the activities of all other agents in the stack.
- A series of domain-specific agents tackle tasks across the network – for example, querying the health of an individual switch – and then report results cohesively to the orchestrator.
- Domain-specific agents then leverage one or more MCP agents to obtain the results from the network desired component.
During an (all-too-brief!) demo, Selector AI showed how a detected problem (shown as a non-green hexagon in main monitoring UI) could be drilled into and then queried in natural language to provide advice on how to fix a particular issue.
Need Tools? Great. Got Tools? Perfect!
The three-level agentic model lets Selector AI offer bespoke solutions to each client in their portfolio, meeting them client where they are right now in their network problem resolution methodology:
- As some clients already have a well-defined toolset for solving network issues, the Selector AI platform acts as an orchestrator to apply those tools directly.
- Alternatively, their platform also lets a client who already has their own orchestration and solution toolsets to leverage the Selector AI models to figure out what’s malfunctioning and – always with a human in the loop where necessary – solve the problem with more precise intelligence than if the client had built their own detection and severity ranking infrastructure.
Selector AI offers a brief demo of its platform’s offerings that was roughly analogous to what they showed us, so grab a look so you can see it in action.
