AI Governance
As agile teams incorporate AI agents into their workflows, governance stops being an abstract matter of corporate policy and becomes part of the team's everyday operational practice.
Dimensions of AI governance
Usage policies
They define which AI tools the team can use, for what types of task, and with what restrictions. They include:
- What data can be provided to an external model (especially relevant with customer data or confidential information).
- What tasks can be delegated to autonomous agents and which require human intervention (HITL/HOTL).
- What AI models or services are authorized.
Autonomy limits
They establish the HITL/HOTL model for each type of agentic action: which actions require prior human approval, which can be executed automatically with subsequent notification, and which are prohibited for agents.
Output auditing
Processes for reviewing and logging the output of AI agents, especially in critical tasks. They include:
- Logs of which agent performed which action and with what instructions.
- Periodic reviews of the quality of AI outputs in production.
- Mechanisms to detect and correct hallucinations or unexpected behaviors.
Accountability
It clearly defines who is responsible for the AI's output. In the Scrum with AI framework, the Scrum Manager guide states that the product builder is responsible for validating AI-generated code before it is integrated, regardless of who (or what) produced it.
The Agile Enabler Role
The agile enabler (the evolution of the Scrum Master in AI-enabled teams) is responsible for AI governance within the team: they design and maintain the policies, facilitate conversations about limits and risks, and ensure that the team is transparently accountable for its use of AI.
Common mistake
Waiting for incidents to happen before defining governance. AI governance is more effective and less costly when it is designed preventively, before agents are in production. A team that starts using autonomous agents without clear policies on autonomy, data, and accountability is not "being agile": it is accumulating operational and regulatory risk that may well lead to an incident.
Resources
🏦 IA aplicada al trabajo ágil [AI Applied to Agile Work, in Spanish]Skill Arena · Scrum Manager
📄 Scrum Master en equipos con IA [Scrum Master in Teams with AI, in Spanish]Free download · Scrum Manager
See also
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