agent-governance

Policy enforcement for AI agents is implemented through declarative policy objects that define granular rules for tool usage, content filtering, and rate limiting to restrict external tool access. You apply these policy objects directly to agent workflows to block unauthorized actions.

Preventing prompt injection in production deployments requires intent classification and pattern-based threat detection to identify harmful prompts before execution. This mechanism intercepts malicious inputs, classifies intent, and blocks potentially dangerous commands from reaching the agent's core logic.

An audit trail for AI agent systems is built by maintaining immutable logs of all agent actions, capturing tool calls, inputs, and outputs for compliance and debugging. This ensures every action executed by the agent is recorded and traceable for regulatory reviews.

Trust scoring tracks agent reliability by assigning scores based on historical behavior and managing multi-agent interactions. This system monitors agent performance over time, enabling you to evaluate reliability and safely route tasks between multiple agents in a workflow.

This AI governance approach works natively with Python, utilizing tool-level decorators and declarative policy objects to secure multi-agent workflows. It integrates directly into Python-based agent systems to apply safety controls, intent classification, and audit logging.

To restrict PII export during database access, define a policy that only allows read operations on specific tables and blocks any attempt to export sensitive information. The policy enforcement layer intercepts tool calls and terminates actions that violate content filtering rules.