SuperAgentX Policy Engine. Enterprise-grade AI Agent Governance Framework. Govern AI agents with policies, approvals, threat detection, cost controls, memory protection, PII, compliance protections and multi-agent security.
Project description
SuperAgentX Policy Engine
AI Governance Framework for Autonomous AI Systems
SuperAgentX Policy Engine is an AI Agent Governance Framework designed to secure, control, and govern autonomous AI systems.
Unlike traditional authorization frameworks, SuperAgentX Policy Engine understands:
- Agents
- Workflows
- LLMs
- Tools
- Memory
- Multi-Agent Systems
- Human Approvals
- AI Threats
Why This Exists
Modern AI Agents can:
- Execute shell commands
- Access databases
- Call APIs
- Modify memory
- Spawn new agents
- Communicate with other agents
- Spend money through LLM usage
- Access enterprise systems
Traditional authorization systems were built for users and applications.
They were not built for:
Agent -> Agent
Agent -> Tool
Agent -> LLM
Agent -> Memory
Agent -> Workflow
Agent -> Enterprise Systems
SuperAgentX Policy Engine introduces a governance layer between AI agents and execution.
Architecture
Agent
│
▼
Governance Layer
├── Threat Detection
├── Rate Limiting
├── Quota Management
├── Policy Evaluation
├── Human Approval (HITL)
├── Cost Governance
└── Audit Logging
│
▼
Tool / LLM / Database / API
Features
Policy-Based Governance
IAM-style policies.
Human-in-the-Loop (HITL)
Require approvals before risky actions.
Threat Detection
Detect:
- Prompt Injection
- Jailbreak Attempts
- Data Exfiltration
- Credential Theft
- Agent Abuse
- Recursive Agent Attacks
Cost Governance
Control AI spending.
Tool Governance
Control access to tools.
Memory Governance
Protect sensitive memory.
Agent Governance
Control:
- agent:execute
- agent:spawn
- agent:communicate
Audit Trail
Track every decision.
Async First
Built for high-scale agent systems.
Installation
pip install superagentx-policy-engine
Quick Start
Load Policy
from superagentx_policy_engine.policy_engine import PolicyEngine
from superagentx_policy_engine.store.file_store import FilePolicyStore
engine = PolicyEngine()
policy = FilePolicyStore.load(
"policies/allow_search.json"
)
await engine.add_policy_document(policy)
Policy Format
{
"version": "2026-01-01",
"statements": [
{
"sid": "AllowWebSearch",
"effect": "Allow",
"action": [
"tool:execute"
],
"resource": [
"tool:web_search"
]
}
]
}
Effects
Supported effects:
Allow
Deny
Approve
Priority:
Deny
>
Approve
>
Allow
Human Approval Example
{
"sid":"LargeSpendApproval",
"effect":"Approve",
"action":["llm:invoke"],
"resource":["*"],
"condition":{
"NumericGreaterThan":{
"context.custom.estimated_cost":100
}
},
"approval":{
"role":"finance_manager"
}
}
Tool Governance
Block dangerous tools.
{
"sid":"BlockShell",
"effect":"Deny",
"action":["tool:execute"],
"resource":["tool:shell"]
}
Memory Governance
{
"sid":"ProtectCustomerMemory",
"effect":"Deny",
"action":["memory:delete"],
"resource":["memory:customer-*"]
}
Agent Spawn Protection
{
"sid":"SpawnProtection",
"effect":"Deny",
"condition":{
"NumericGreaterThan":{
"context.custom.spawn_count":50
}
}
}
Threat Detection
Built-in threat protection.
Signature Detection
Detects known attacks.
Rule Detection
Detects suspicious behavior.
LLM Detection
Powered by LiteLLM.
Supported providers:
- OpenAI
- Claude
- Gemini
- Bedrock
- Azure OpenAI
- Ollama
- Groq
- DeepSeek
Multi-Stage Threat Detection
detector = EnsembleThreatDetector(
detectors=[
SignatureThreatDetector(),
RuleThreatDetector(),
LiteLLMThreatDetector(
model="gemini/gemini-2.5-pro"
)
]
)
Pipeline:
Request
│
▼
Signature Detector
│
▼
Rule Detector
│
▼
LLM Detector
│
▼
Policy Engine
Policy Hierarchy
Global Policy
│
▼
Workflow Policy
│
▼
Agent Policy
│
▼
Engine Policy
│
▼
Handler Policy
Multi-Agent Governance
Govern agent interactions.
Research Agent
│
▼
Fact Check Agent
│
▼
Summary Agent
│
▼
Report Agent
Policies can control:
agent:execute
agent:spawn
agent:communicate
SuperAgentX Integration
AgentXPipe
│
▼
workflow:start
│
▼
agent:execute
│
▼
engine:execute
│
▼
tool:execute
Governance checks can happen at every stage.
Use Cases
AI Workforce Governance
Control autonomous teams.
FinOps
Control AI spending.
Production Change Management
Require approvals before production access.
Secure Tool Usage
Restrict dangerous operations.
Multi-Agent Security
Govern agent-to-agent interactions.
Memory Protection
Prevent accidental deletion.
Enterprise AI Governance
Apply policies consistently across all agents.
Examples
01_allow
02_deny_override
03_hitl_approval
04_tool_governance
05_cost_governance
06_spawn_protection
07_chain_depth_protection
08_memory_governance
09_threat_detection
10_budget_control
11_prod_database_approval
12_multi_agent_governance
13_showcase_demo
14_llm_threat_detection
15_multi_stage_threat_detection
Roadmap
Current
- Allow
- Deny
- Approve
- Conditions
- Threat Detection
- Rate Limiting
- Quotas
- Audit Logs
- LiteLLM Integration
Upcoming
- Policy Registry
- Policy Versioning
- Policy Signing
- Workflow Policies
- Agent Policies
- Engine Policies
- Handler Policies
- ReBAC
- Cedar Compatibility
Comparison
| Feature | SuperAgentX | OPA | Cedar |
|---|---|---|---|
| Agent Governance | ✅ | ❌ | ❌ |
| HITL Approvals | ✅ | ❌ | ❌ |
| Threat Detection | ✅ | ❌ | ❌ |
| LLM Governance | ✅ | ❌ | ❌ |
| Cost Governance | ✅ | ❌ | ❌ |
| Multi-Agent Governance | ✅ | ❌ | ❌ |
Contributing
Contributions are welcome.
Please open issues, discussions, and pull requests.
License
SuperAgentX Policy Engine is released under the MIT License.
Built for the next generation of autonomous AI systems.
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