Human oversight
Sensitive or irreversible actions can require a person. Agents are given jobs and limits, not a blank cheque.
Responsible AI
AI that can be operated, inspected and constrained
Useful enterprise AI is governed AI. These are the practices we design into systems that can retrieve knowledge or take action.
Practices
Eight commitments, built into the architecture
Privacy
Data minimization, purpose limitation and source permissions are design inputs for retrieval and logging.
Transparency
Users should understand when they are working with an AI system and what sources an answer is grounded in.
Data security
Identity, encryption in transit, and least-privilege access to tools the model can call.
Model evaluation
Quality is measured against real tasks — not only a demo prompt that happened to work.
Monitoring
Production systems need traces, cost controls and a way to detect drift or abuse.
Hallucination mitigation
Retrieval, refusals, citations and evaluation reduce confident answers that are not supported.
Access control
Answers and actions follow who the user is allowed to be — including document-level permissions.
Where the controls sit
Every step of the agent loop is a control point
Authentication before the event is accepted, permissions on knowledge, allow-lists on tools, and approval before an action is taken.
01
User / Event
02
AI Agent
03
Reasoning
04
Knowledge
05
Tools
06
Action
Tools
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