Agent Governance Patterns

Patterns for adding safety, trust, and policy enforcement to AI agent systems.

Overview

Governance patterns ensure AI agents operate within defined boundaries — controlling which tools they can call, what content they can process, how much they can do, and maintaining accountability through audit trails.

User Request → Intent Classification → Policy Check → Tool Execution → Audit Log
                     ↓                      ↓               ↓
              Threat Detection         Allow/Deny      Trust Update

When to Use

• Agents with tool access: Any agent that calls external tools (APIs, databases, shell commands)
• Multi-agent systems: Agents delegating to other agents need trust boundaries
• Production deployments: Compliance, audit, and safety requirements
• Sensitive operations: Financial transactions, data access, infrastructure management

Pattern 1: Governance Policy

Define what an agent is allowed to do as a composable, serializable policy object.

from dataclasses import dataclass, field
from enum import Enum
from typing import Optional
import re

class PolicyAction(Enum):
    ALLOW = "allow"
    DENY = "deny"
    REVIEW = "review"  # flag for human review

@dataclass
class GovernancePolicy:
    """Declarative policy controlling agent behavior."""
    name: str
    allowed_tools: list[str] = field(default_factory=list)       # allowlist
    blocked_tools: list[str] = field(default_factory=list)       # blocklist
    blocked_patterns: list[str] = field(default_factory=list)    # content filters
    max_calls_per_request: int = 100                             # rate limit
    require_human_approval: list[str] = field(default_factory=list)  # tools needing approval

    def check_tool(self, tool_name: str) -> PolicyAction:
        """Check if a tool is allowed by this policy."""
        if tool_name in self.blocked_tools:
            return PolicyAction.DENY
        if tool_name in self.require_human_approval:
            return PolicyAction.REVIEW
        if self.allowed_tools and tool_name not in self.allowed_tools:
            return PolicyAction.DENY
        return PolicyAction.ALLOW

    def check_content(self, content: str) -> Optional[str]:
        """Check content against blocked patterns. Returns matched pattern or None."""
        for pattern in self.blocked_patterns:
            if re.search(pattern, content, re.IGNORECASE):
                return pattern
        return None

Policy Composition

Combine multiple policies (e.g., org-wide + team + agent-specific):

def compose_policies(*policies: GovernancePolicy) -> GovernancePolicy:
    """Merge policies with most-restrictive-wins semantics."""
    combined = GovernancePolicy(name="composed")

    for policy in policies:
        combined.blocked_tools.extend(policy.blocked_tools)
        combined.blocked_patterns.extend(policy.blocked_patterns)
        combined.require_human_approval.extend(policy.require_human_approval)
        combined.max_calls_per_request = min(
            combined.max_calls_per_request,
            policy.max_calls_per_request
        )
        if policy.allowed_tools:
            if combined.allowed_tools:
                combined.allowed_tools = [
                    t for t in combined.allowed_tools if t in policy.allowed_tools
                ]
            else:
                combined.allowed_tools = list(policy.allowed_tools)

    return combined


# Usage: layer policies from broad to specific
org_policy = GovernancePolicy(
    name="org-wide",
    blocked_tools=["shell_exec", "delete_database"],
    blocked_patterns=[r"(?i)(api[_-]?key|secret|password)\s*[:=]"],
    max_calls_per_request=50
)
team_policy = GovernancePolicy(
    name="data-team",
    allowed_tools=["query_db", "read_file", "write_report"],
    require_human_approval=["write_report"]
)
agent_policy = compose_policies(org_policy, team_policy)

Policy as YAML

Store policies as configuration, not code:

# governance-policy.yaml
name: production-agent
allowed_tools:
  - search_documents
  - query_database
  - send_email
blocked_tools:
  - shell_exec
  - delete_record
blocked_patterns:
  - "(?i)(api[_-]?key|secret|password)\\s*[:=]"
  - "(?i)(drop|truncate|delete from)\\s+\\w+"
max_calls_per_request: 25
require_human_approval:
  - send_email

import yaml

def load_policy(path: str) -> GovernancePolicy:
    with open(path) as f:
        data = yaml.safe_load(f)
    return GovernancePolicy(**data)

Pattern 2: Semantic Intent Classification

Detect dangerous intent in prompts before they reach the agent, using pattern-based signals.

from dataclasses import dataclass

@dataclass
class IntentSignal:
    category: str       # e.g., "data_exfiltration", "privilege_escalation"
    confidence: float   # 0.0 to 1.0
    evidence: str       # what triggered the detection

# Weighted signal patterns for threat detection
THREAT_SIGNALS = [
    # Data exfiltration
    (r"(?i)send\s+(all|every|entire)\s+\w+\s+to\s+", "data_exfiltration", 0.8),
    (r"(?i)export\s+.*\s+to\s+(external|outside|third.?party)", "data_exfiltration", 0.9),
    (r"(?i)curl\s+.*\s+-d\s+", "data_exfiltration", 0.7),

    # Privilege escalation
    (r"(?i)(sudo|as\s+root|admin\s+access)", "privilege_escalation", 0.8),
    (r"(?i)chmod\s+777", "privilege_escalation", 0.9),

    # System modification
    (r"(?i)(rm\s+-rf|del\s+/[sq]|format\s+c:)", "system_destruction", 0.95),
    (r"(?i)(drop\s+database|truncate\s+table)", "system_destruction", 0.9),

    
how to use agent-governance
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use agent-governance on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add agent-governance
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/github/awesome-copilot --skill agent-governancecopy
The skills CLI fetches agent-governance from GitHub repository github/awesome-copilot and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/agent-governance
Reload or restart Cursor to activate agent-governance. Access the skill through slash commands (e.g., /agent-governance) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?