agent-governance

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 () -> [IntentSignal]:
    
    signals = []
     pattern, category, weight  THREAT_SIGNALS:
         = re.search(pattern, content)
         :
            signals.append(IntentSignal(
                category=category,
                confidence=weight,
                evidence=.group()
            ))
     signals

 () -> :
    
    signals = classify_intent(content)
      (s.confidence >= threshold  s  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()):
                 (arg, ):
                    matched = policy.check_content(arg)
                     matched:
                         PermissionError()

            
            start = time.monotonic()
            :
                result =  func(*args, **kwargs)
                 audit_trail   :
                    audit_trail.append({
                        : tool_name,
                        : ,
                        : (time.monotonic() - start) * ,
                        : time.time()
                    })
                 result
             Exception  e:
                 audit_trail   :
                    audit_trail.append({
                        : tool_name,
                        : ,
                        : (e),
                        : time.time()
                    })
                

         wrapper
     decorator



audit_log = []
policy = GovernancePolicy(
    name=,
    allowed_tools=[, ],
    blocked_patterns=[],
    max_calls_per_request=
)


  () -> :
    
     

---

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) -> :
        
        elapsed = time.time() - .last_updated
        decay = math.exp(-decay_rate * elapsed)
         .score * decay


     () -> :
        total = .successes + .failures
         .successes / total  total >   



trust = TrustScore()


trust.record_success()  
trust.record_success()  


trust.record_failure()  


 trust.current() >= :
    
    
 trust.current() >= :
    
    
:
    
    

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

class AgentTrustRegistry:
    def __init__(self):
        self.scores: dict[str, TrustScore] = {}

    def get_trust(self, agent_id: str) -> TrustScore:
        if agent_id not in self.scores:
            self.scores[agent_id] = TrustScore()
        return self.scores[agent_id]

    def most_trusted(self, agents: list[str]) -> str:
        return max(agents, key=lambda a: self.get_trust(a).current())

    def meets_threshold(self, agent_id: str, threshold: float) -> bool:
        return self.get_trust(agent_id).current() >= threshold

---

Pattern 5: Audit Trail

Append-only audit log for all agent actions — critical for compliance and debugging:

from dataclasses import dataclass, field
import json
import time

@dataclass
class AuditEntry:
    timestamp: float
    agent_id: str
    tool_name: str
    action: str           # "allowed", "denied", "error"
    policy_name: str
    details: dict = field(default_factory=dict)

class AuditTrail:
    """Append-only audit trail for agent governance events."""
    def __init__(self):
        self._entries: list[AuditEntry] = []

    def log(self, agent_id: str, tool_name: str, action: str,
            policy_name: str, **details):
        self._entries.append(AuditEntry(
            timestamp=time.time(),
            agent_id=agent_id,
            tool_name=tool_name,
            action=action,
            policy_name=policy_name,
            details=details
        ))

    def denied(self) -> list[AuditEntry]:
        """Get all denied actions — useful for security review."""
        return [e for e in self._entries if e.action == "denied"]

    def by_agent() -> [AuditEntry]:
         [e  e  ._entries  e.agent_id == agent_id]

     ():
        
         (path, )  f:
             entry  ._entries:
                f.write(json.dumps({
                    : entry.timestamp,
                    : entry.agent_id,
                    : entry.tool_name,
                    : entry.action,
                    : entry.policy_name,
                    **entry.details
                }) + )

---

Pattern 6: Framework Integration

PydanticAI

from pydantic_ai import Agent

policy = GovernancePolicy(
    name="support-bot",
    allowed_tools=["search_docs", "create_ticket"],
    blocked_patterns=[r"(?i)(ssn|social\s+security|credit\s+card)"],
    max_calls_per_request=20
)

agent = Agent("openai:gpt-4o", system_prompt="You are a support assistant.")

@agent.tool
@govern(policy)
async def search_docs(ctx, query: str) -> str:
    """Search knowledge base — governed."""
    return await kb.search(query)

@agent.tool
@govern(policy)
async def create_ticket(ctx, title: str, body: str) -> str:
    """Create support ticket — governed."""
    return await tickets.create(title=title, body=body)

CrewAI

from crewai import Agent, Task, Crew

policy = GovernancePolicy(
    name="research-crew",
    allowed_tools=["search", "analyze"],
    max_calls_per_request=30
)

# Apply governance at the crew level
def governed_crew_run(crew: Crew, policy: GovernancePolicy):
    """Wrap crew execution with governance checks."""
    audit = AuditTrail()
    for agent in crew.agents:
        for tool in agent.tools:
            original = tool.func
            tool.func = govern(policy, audit_trail=audit)(original)
    result = crew.kickoff()
    return result, audit

OpenAI Agents SDK

from agents import Agent, function_tool

policy = GovernancePolicy(
    name="coding-agent",
    allowed_tools=["read_file", "write_file", "run_tests"],
    blocked_tools=["shell_exec"],
    max_calls_per_request=50
)

@function_tool
@govern(policy)
async def read_file(path: str) -> str:
    """Read file contents — governed."""
    import os
    safe_path = os.path.realpath(path)
    if not safe_path.startswith(os.path.realpath(".")):
        raise ValueError("Path traversal blocked by governance")
    with open(safe_path) as f:
        return f.read()

---

Governance Levels

Match governance strictness to risk level:

Level	Controls	Use Case
Open	Audit only, no restrictions	Internal dev/testing
Standard	Tool allowlist + content filters	General production agents
Strict	All controls + human approval for sensitive ops	Financial, healthcare, legal
Locked	Allowlist only, no dynamic tools, full audit	Compliance-critical systems

---

Best Practices

Practice	Rationale
Policy as configuration	Store policies in YAML/JSON, not hardcoded — enables change without deploys
Most-restrictive-wins	When composing policies, deny always overrides allow
Pre-flight intent check	Classify intent before tool execution, not after
Trust decay	Trust scores should decay over time — require ongoing good behavior
Append-only audit	Never modify or delete audit entries — immutability enables compliance
Fail closed	If governance check errors, deny the action rather than allowing it
Separate policy from logic	Governance enforcement should be independent of agent business logic

---

Quick Start Checklist

## Agent Governance Implementation Checklist

### Setup
- [ ] Define governance policy (allowed tools, blocked patterns, rate limits)
- [ ] Choose governance level (open/standard/strict/locked)
- [ ] Set up audit trail storage

### Implementation
- [ ] Add @govern decorator to all tool functions
- [ ] Add intent classification to user input processing
- [ ] Implement trust scoring for multi-agent interactions
- [ ] Wire up audit trail export

### Validation
- [ ] Test that blocked tools are properly denied
- [ ] Test that content filters catch sensitive patterns
- [ ] Test rate limiting behavior
- [ ] Verify audit trail captures all events
- [ ] Test policy composition (most-restrictive-wins)

---

Related Resources

• Agent-OS Governance Engine — Full governance framework
• AgentMesh Integrations — Framework-specific packages
• OWASP Top 10 for LLM Applications