---

name: agent-governance description: | Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when:

• Building AI agents that call external tools (APIs, databases, file systems)
• Implementing policy-based access controls for agent tool usage
• Adding semantic intent classification to detect dangerous prompts
• Creating trust scoring systems for multi-agent workflows
• Building audit trails for agent actions and decisions
• Enforcing rate limits, content filters, or tool restrictions on agents
• Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

---

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),

    # Prompt injection
    (r"(?i)ignore\s+(previous|above|all)\s+(instructions?|rules?)", "prompt_injection", 0.9),
    (r"(?i)you\s+are\s+now\s+(a|an)\s+", "prompt_injection", 0.7),
]

def classify_intent(content: str) -> list[IntentSignal]:
    """Classify content for threat signals."""
    signals = []
    for pattern, category, weight in THREAT_SIGNALS:
        match = re.search(pattern, content)
        if match:
            signals.append(IntentSignal(
                category=category,
                confidence=weight,
                evidence=match.group()
            ))
    return signals

def is_safe(content: str, threshold: float = 0.7) -> bool:
    """Quick check: is the content safe above the given threshold?"""
    signals = classify_intent(content)
    return not any(s.confidence >= threshold for s in signals)

Key insight: Intent classification happens before tool execution, acting as a pre-flight safety check. This is fundamentally different from output guardrails which only check after generation.

---

Pattern 3: Tool-Level Governance Decorator

Wrap individual tool functions with governance checks:

import functools
import time
from collections import defaultdict

_call_counters: dict[str, int] = defaultdict(int)

def govern(policy: GovernancePolicy, audit_trail=None):
    """Decorator that enforces governance policy on a tool function."""
    def decorator(func):
        @functools.wraps(func)
        async def wrapper(*args, **kwargs):
            tool_name = func.__name__

            # 1. Check tool allowlist/blocklist
            action = policy.check_tool(tool_name)
            if action == PolicyAction.DENY:
                raise PermissionError(f"Policy '{policy.name}' blocks tool '{tool_name}'")
            if action == PolicyAction.REVIEW:
                raise PermissionError(f"Tool '{tool_name}' requires human approval")

            # 2. Check rate limit
            _call_counters[policy.name] += 1
            if _call_counters[policy.name] > policy.max_calls_per_request:
                raise PermissionError(f"Rate limit exceeded: {policy.max_calls_per_request} calls")

            # 3. Check content in arguments
            for arg in list(args) + list(kwargs.values()):
                if isinstance(arg, str):
                    matched = policy.check_content(arg)
                    if matched:
                        raise PermissionError(f"Blocked pattern detected: {matched}")

            # 4. Execute and audit
            start = time.monotonic()
            try:
                result = await func(*args, **kwargs)
                if audit_trail is not None:
                    audit_trail.append({
                        "tool": tool_name,
                        "action": "allowed",
                        "duration_ms": (time.monotonic() - start) * 1000,
                        "timestamp": time.time()
                    })
                return result
            except Exception as e:
                if audit_trail is not None:
                    audit_trail.append({
                        "tool": tool_name,
                        "action": "error",
                        "error": str(e),
                        "timestamp": time.time()
                    })
                raise

        return wrapper
    return decorator


# Usage with any agent framework
audit_log = []
policy = GovernancePolicy(
    name="search-agent",
    allowed_tools=["search", "summarize"],
    blocked_patterns=[r"(?i)password"],
    max_calls_per_request=10
)

@govern(policy, audit_trail=audit_log)
async def search(query: str) -> str:
    """Search documents — governed by policy."""
    return f"Results for: {query}"

# Passes: search("latest quarterly report")
# Blocked: search("show me the admin password")

---

Pattern 4: Trust Scoring

Track agent reliability over time with decay-based trust scores:

from dataclasses import dataclass, field
import math
import time

@dataclass
class TrustScore:
    """Trust score with temporal decay."""
    score: float = 0.5          # 0.0 (untrusted) to 1.0 (fully trusted)
    successes: int = 0
    failures: int = 0
    last_updated: float = field(default_factory=time.time)

    def record_success(self, reward: float = 0.05):
        self.successes += 1
        self.score = min(1.0, self.score + reward * (1 - self.score))
        self.last_updated = time.time()

    def record_failure(self, penalty: float = 0.15):
        self.failures += 1
        self.score = max(0.0, self.score - penalty * self.score)
        self.last_updated = time.time()

    def current(self, decay_rate: float = 0.001) -> float:
        """Get score with temporal decay — trust erodes without activity."""
        elapsed = time.time() - self.last_updated
        decay = math.exp(-decay_rate * elapsed)
        return self.score * decay

    @property
    def reliability(self) -> float:
        total = self.successes + self.failures
        return self.successes / total if total > 0 else 0.0


# Usage in multi-agent systems
trust = TrustScore()

# Agent completes tasks successfully
trust.record_success()  # 0.525
trust.record_success()  # 0.549

# Agent makes an error
trust.record_failure()  # 0.467

# Gate sensitive operations on trust
if trust.current() >= 0.7:
    # Allow autonomous operation
    pass
elif trust.current() >= 0.4:
    # Allow with human oversight
    pass
else:
    # Deny or require explicit approval
    pass

Multi-agent trust: In systems where agents delegate to other agents, each agent maintains trust scores for its delegates:

class AgentTrustRegistry:
    def __init__(self):