Every downstream control has a gap, and agents retry until they find one. WoolyAI reads internal signals from the model while it thinks, generates, and acts on the output before the agent receives it.
Agents retry. When one path fails, an agent looks for another. That helps it finish a task. It also means you cannot assume every downstream control will hold.
Most teams control the gap today with guardrails and agent governance. Both check at the edges. Model governance adds a check at the model.
How teams do it now
Guardrails
How teams do it now
Agent governance
With WoolyAI
Model governance
The question
Is this prompt or output allowed?
Is this agent allowed to perform this action?
Is the model allowed to generate this behavior or data at all?
Where it checks
The prompt going in and the output coming out
Tools, permissions, sandbox, and network
Telemetry from the model’s neurons
When it acts
Before or after generation
When the agent attempts the action
During generation, before the agent receives it