AI Agent Governance
AgentShield
Averon product
Know exactly what your AI agents did, and prove it.
AgentShield instruments AI agents and turns every tool call, model call, success and failure into a tamper-evident audit record, with risk classification, anomaly alerts, a human-approval trail and signed evidence packages.
- The problem
- AI agents increasingly read customer data, trigger financial operations and change production systems. When something goes wrong, or an auditor, customer or investor asks, most teams cannot reconstruct what an agent actually did, let alone prove the record has not been altered. Application logs were never designed to be evidence.
- Built for
- Engineering and platform teams running AI agents in production, particularly those already being asked how agent behaviour is overseen and audited.
- Instrument any agent
- Tamper-evident audit ledger
- Risk classification & anomaly alerts
- Human approval trail
- Signed evidence packages
- Session replay dashboard
View AgentShield Python SDK on PyPI
agent.py
from agentshield_control_plane import AgentShield
shield = AgentShield(api_key="ags_live_xxxx",
endpoint="http://localhost:8000")
with shield.session(agent="my-agent") as session:
with session.tool_call("search", query="weather in SF"):
... # your real tool-call code
with session.llm_call("gpt-5", tokens=120):
... # your real LLM call- SDK
- Ingestion
- Risk classification
- Anomaly detection
- Hash-chained ledger
- Signed evidence
