Human-in-the-Loop as a First-Class Primitive
A designed collaboration model between humans and agents
Part VI: Human-in-the-Loop as a First-Class Primitive
It's tempting to describe an Operating Context System in terms of what the agents do. But the system only works because of what humans do.
This is not "AI plus some humans checking things." It's a designed collaboration model between humans and agents, with explicit decision rights over context.
| Decisions That Require Humans | Work Better Suited to Agents |
|---|---|
| Defining the operating model | Monitoring at scale across more signals than any human could track |
| Resolving ambiguity about whether two things are actually the same | Pattern recognition across large datasets |
| Determining what qualifies as an anchor artifact | Routine connection-making between well-defined artifacts |
| Evaluating whether an agent-generated insight is meaningful | Drafting updates and readouts for human review |
| Deciding when the operating model itself needs to change | Flagging anomalies and risks for human attention |
The escalation path between agents and humans is a design decision that must be made explicitly. An agent that flags something as "at risk" needs a clear path to human attention. A human who disagrees with an agent-generated connection needs a clear way to correct it that feeds back into the agent's future accuracy.
The operating context is not something that runs itself without human oversight. It requires far less human maintenance than a purely manual system, and the maintenance it does require is higher-leverage, focused on design and judgment rather than data entry.