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Agent Governance Toolkit — Live Governance Demo

Demonstrates real-time governance enforcement using real LLM calls (OpenAI / Azure OpenAI) with the full governance middleware stack. Every API call, policy decision, and audit entry is real.

What This Shows

Scenario Layer What Happens
1. Policy Enforcement GovernancePolicyMiddleware YAML policy allows a search prompt but blocks **/internal/** — before the LLM is called
2. Capability Sandboxing CapabilityGuardMiddleware LLM requests tool calls; governance allows run_code but denies write_file
3. Rogue Detection RogueDetectionMiddleware Behavioral anomaly engine detects a 50-call burst and auto-quarantines
4. Content Filtering GovernancePolicyMiddleware Multiple prompts evaluated — dangerous ones blocked, safe ones forwarded
Audit Trail AuditLog + Merkle chain Every decision is cryptographically chained and verifiable

Architecture

+-------------------------------------------------------+
|  Agent (with real OpenAI / Azure OpenAI backend)      |
|  +--------------------------------------------------+ |
|  |  GovernancePolicyMiddleware (YAML policy eval)    | <-- Blocks before LLM
|  |  CapabilityGuardMiddleware  (tool allow/deny)     | <-- Intercepts tools
|  |  RogueDetectionMiddleware   (anomaly scoring)     | <-- Behavioral SRE
|  +--------------------------------------------------+ |
|                      |                                |
|         Real LLM API Call (gpt-4o-mini)               |
+-------------------------------------------------------+
        |                              |
        v                              v
  AuditLog (Merkle)           RogueAgentDetector
  agentmesh.governance        agent_sre.anomaly

Prerequisites

pip install agent-os-kernel agentmesh-platform agent-sre openai

# Set your API key (pick one)
export OPENAI_API_KEY="sk-..."
# or for Azure OpenAI:
export AZURE_OPENAI_API_KEY="..."
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com"

Running

cd agent-governance-toolkit

# Default (gpt-4o-mini, ~$0.01 per run)
python demo/maf_governance_demo.py

# Use a specific model
python demo/maf_governance_demo.py --model gpt-4o

# Show raw LLM responses
python demo/maf_governance_demo.py --verbose

Key Files

File Purpose
demo/maf_governance_demo.py Main demo script (real LLM calls)
demo/policies/research_policy.yaml Declarative governance policy
demo/governance-dashboard/ Real-time Streamlit dashboard — fleet overview, shadow agents, lifecycle, policy feed, trust heatmap
packages/agent-os/src/agent_os/integrations/maf_adapter.py Governance middleware
packages/agent-mesh/src/agentmesh/governance/audit.py Merkle-chained audit log
packages/agent-sre/src/agent_sre/anomaly/rogue_detector.py Rogue agent detector

Governance Dashboard

For a visual overview of your agent fleet:

cd demo/governance-dashboard
pip install -r requirements.txt
streamlit run app.py
# or: docker-compose up

See the dashboard README for details.

Links