An Implementation of the Microsoft Agent Governance Toolkit for Safe AI Agent Tool Use with Policies, Approvals, Audit Logs, and Risk Controls

["In this tutorial, we build a governed AI-agent workflow using Microsoft's Agent Governance Toolkit as the reference point. We create a Colab-ready implementation where agents do not directly execute tools; instead, every action first passes through a governance layer that checks the agent's identity, trust score, risk tier, requested tool, action type, sensitivity level, and policy rules.", 'We define a YAML-based policy that controls destructive database operations, external email sending, shell execution, access to sensitive data, and financial transfers. We then wrap each tool with governance logic so that actions can be allowed, denied, sandboxed, or routed through an approval step before execution.

We also generate tamper-evident audit records, run policy tests, activate a kill switch, summarize governance decisions, and visualize the relationships between agents, tools, rules, and outcomes as a graph.', 'We set up the Colab environment by installing the required libraries and importing everything needed for policy handling, auditing, visualization, and data analysis. We also clone the Microsoft Agent Governance Toolkit repository to keep the notebook connected to the original project. We then try to import the official governance function, while keeping the tutorial runnable even if the preview package changes.', 'We create a YAML governance policy that defines how agent actions should be handled before execution.

We add rules to block destructive database actions, require approval for external emails and financial transfers, sandbox shell commands, and restrict low-trust agents from sensitive data. We then save and reload this policy so the rest of the tutorial can use it as the main governance configuration.', 'We define the core data structures for representing agent identities, governance decisions, and governance-related exceptions. We also create a small dot-access dictionary helper so that policy conditions can read values such as action.type and identity.trust_score.

We then build a safe condition evaluator that checks whether each policy rule matches the current agent action. We implement a tamper-evident audit log that records every governance decision made by the system.', "In conclusion, we have a fully governed-agent workflow that covers both policy enforcement and observability. We simulated multiple agents with varying trust levels.

We showed how the same system responds differently depending on the agent's identity, the action's sensitivity, and the rules defined in the policy file. Safe actions, such as simple database reads, are executed. In contrast, risky actions, such as destructive database changes, unsafe shell commands, low-trust sensitive access, and large financial transfers, are blocked or sent for approval."]