Building CoSignal- An Agentic Capability Risk Detection System
For a long time, I have been fascinated by agentic workflows. Like…too long. In outright wonderment like…I wonder if I will EVER learn to use these? They were mysterious. Almost magical. Scary even. Like little robot wizards.
Like many people exploring AI, I spent years thinking about what was possible — intelligent systems, learning agents, automation pipelines, knowledge preservation, workforce capability modeling — but I had not yet built the portfolio project that truly brought those ideas together. I was always making excuses!
I realized something important: I did not need another idea.
I needed evidence.
As someone working at the intersection of Learning Design, instructional systems, AI, and workforce capability development, I kept returning to a problem I have seen repeatedly across industries:
Organizations lose critical knowledge every day.
Jason Boursier
A warehouse supervisor retires. A high-performing employee leaves. A subject matter expert moves on. A technology rollout struggles because training completion looked good, but performance transfer never happened.
The knowledge disappears.
The capability gap emerges later.
Leadership asks why performance declined after the damage is already visible.
That realization led me to build CoSignal.
CoSignal is an agentic Capability Risk Detection System designed to surface workforce performance risks before they become operational failures. Rather than focusing only on course completion or training outputs, CoSignal asks a different question:
“What signals tell us capability problems are emerging right now?”
For the first proof of concept, I grounded the project in a hypothetical but realistic Microsoft Dynamics 365 warehouse implementation scenario — an environment where change fatigue, operational pressure, support ticket volume, confidence gaps, and process confusion often converge.
The system ingests multiple workforce signals:
• Performance metrics
• Support ticket trends
• Survey sentiment
• LMS analytics
• SME observations
• Operational indicators
From there, an agentic pipeline detects emerging risks such as:
• Knowledge decay
• Process confusion
• SME bottlenecks
• Change resistance
• Confidence gaps
Then it generates targeted interventions designed to improve capability before operational performance declines.
The result is not simply AI-generated training recommendations.
It is measurement-first learning design.
It is performance support informed by evidence.
It is instructional design operating alongside systems thinking.
Building CoSignal represents a shift for me personally.
Less dreaming.
More building.
Less asking what AI might do.
More proving what thoughtful human-centered AI systems can accomplish when paired with learning science, evaluation strategy, and workforce capability thinking.
This is Version 1.
More importantly:
It is built. And there will be many more agentic workflows to come. Sometimes you have to stop thinking and build. That has been my mission lately.
CoSignal is an agentic Capability Risk Detection System designed to surface workforce performance risks before they become operational failures.
Jason Boursier
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