Colab tutorial shows how to use Microsoft Agent Governance Toolkit to vet AI Agent Tool calls

A Colab — ready tutorial demonstrates how to integrate the Microsoft Agent Governance Toolkit to interpose a governance layer between AI agents and external tools, intercepting agent — initiated actions so decisions can be evaluated before any tool call proceeds. The notebook shows this enforcement in practice rather than allowing agents to execute tools directly, making it possible to block or reroute risky actions prior to execution.

The tutorial defines a YAML-based policy that restricts destructive database operations, external email sending, shell execution, access to sensitive data and financial transfers. In the example each tool is wrapped with governance logic so attempted actions may be allowed, denied, sandboxed or routed to a human approval step; the demo also produces tamper — evident audit records and includes policy testing to validate rules and expected outcomes.

To run the demo the Colab installs Python libraries (pyyaml, pandas, networkx, matplotlib, rich) and attempts to install the agent — governance-toolkit package, then clones the Microsoft toolkit repository into the notebook environment. The code tries to import agentmesh.governance.govern while keeping the notebook runnable even if the preview package import fails, enabling hands — on experimentation whether the package is available or falls back to the cloned repository.

Operational controls shown in the implementation include a kill switch, summarized governance decisions and graph visualizations of agents, tools, rules and outcomes. Those features are presented to help teams audit decisions, route risky actions for human approval and visualize relationships among agents, tools and policies before permitting execution — a practical setup for organizations wanting observable, testable guardrails for agent — driven workflows.