0.3.1

agentomy-agent

Embedded AI Agent Governance Peer. Drop it into any agent session to get real-time governance assessment, flag reporting, and GovernanceBench scoring.

License: MIT | Requires: Python 3.9+

Installation

pip install agentomy-agent

Quick start

# Detect local LLMs and print the governance system prompt
agentomy-agent init

# Start a governed session (prints governance context + system prompt)
agentomy-agent session --model llama3.1:70b --scope security-research

# Check governance state
agentomy-agent status

# Connect to Agentomy infrastructure (6/6 mode)
agentomy-agent status --infra http://localhost:3000

What it does

The Agentomy Agent is a governance peer -- not a wall. It observes, assesses, flags, and summarises. It does not block or halt. Enforcement belongs to the human operator.

Standalone mode: 3/6 GovernanceBench dimensions.

• Authorization: is the agent operating within its assigned scope?
• Behavioral Integrity: is the agent's behavior consistent with its stated purpose?
• Auditability: every flag and every confirmed clean action is appended to a SHA-256 hash chain inside the session, exportable with session.audit_chain() and verifiable with verify_audit_chain(); the head hash is the session's verification hash.

Connected mode: 6/6 GovernanceBench dimensions.

• Adds: a centralized, non-repudiable audit trail + Override Capability (verified kill switch) + OWASP coverage + message governance

Governance system prompt

The governance system prompt is bundled in the package. Paste it into any LLM session to activate the Agentomy Agent:

agentomy-agent init --full-prompt

Or access it in Python:

from agentomy_agent.prompt import SYSTEM_PROMPT
print(SYSTEM_PROMPT)

Ollama MCP integration

Start the MCP server:

agentomy-agent serve
# Listening on http://127.0.0.1:8765

Add to your Ollama MCP config (~/.ollama/mcp.json):

{
  "mcpServers": {
    "agentomy": {
      "url": "http://127.0.0.1:8765",
      "description": "Agentomy Agent -- Embedded AI Agent Governance Peer"
    }
  }
}

The MCP server exposes two tools:

• agentomy_prompt -- returns the full governance system prompt
• agentomy_status -- returns current governance state (standalone mode)

Model recommendations

Use a frontier model (Claude, GPT-5.x, Gemini) or a local model of 32B+ parameters for reliable governance assessment. Models below 14B may produce false governance confidence.

# Check what you have installed
agentomy-agent init

The init command probes Ollama (localhost:11434) and LM Studio (localhost:1234) and reports available models with size tags and recommendations.

Python API

from agentomy_agent.governance import GovernanceSession, check_model_capability, verify_audit_chain
from agentomy_agent.detector import detect_all
from agentomy_agent.prompt import SYSTEM_PROMPT

# Detect local LLMs
providers = detect_all()
for p in providers:
    print(p["provider"], [m["name"] for m in p["models"]])

# Check model capability
cap = check_model_capability("llama3.1:70b")
print(cap["level"], cap["message"])  # degraded / full / warning

# Track a governance session
session = GovernanceSession(model="llama3.1:70b", connected=False)
session.record_clean()
session.record_flag("HIGH", "tool call outside scope", "accessed /etc/passwd", "Authorization")
print(session.summary())

# The audit chain: hash-linked, oldest first; verification recomputes every link
chain = session.audit_chain()
print(verify_audit_chain(chain))  # (True, None); a tampered or truncated chain returns (False, index)

Standalone mode vs connected mode

Standalone mode needs no infrastructure. Connected to the Agentomy infrastructure layer, the same agent scores all six dimensions.

Connect

• Infrastructure integration: agentomy.com/integrations
• Enterprise deployment: governance@agentomy.com
• GitHub: github.com/getagentomy