Execution gatekeeper for AI agents
Policy-driven governance, risk scoring, and human-in-the-loop approval for AI agent tool calls. Built for production with modern web UI, comprehensive CLI, and SDK integrations.
Action Log
High-Risk Action
Requires approval before execution
Guardrails, auditability, and enterprise integrations
Unified controls across agents, clouds, and internal systems.
Policy-as-Code Guardrails
Centrally manage allow, deny, and require_approval rules to control agent actions in real time.
Audit-Ready Trails
Every action is logged, timestamped, and signed for traceability across teams and environments.
Enterprise Integrations
Connect to your stack—SSO, SIEM, cloud providers, SDKs—without changing developer workflows.
Designed for high-trust workflows
Apply the same guardrails to agents, automation, and LLM tools.
Governed AI Agents
Enforce approvals and risk thresholds before agents run sensitive operations.
Secure Automation
Wrap RPA and LLM tools with policy checks, audit trails, and live approvals.
Regulated Workloads
Meet SOC 2 and ISO requirements with immutable logs and least-privilege policies.
Built for regulated teams
Zero-trust guardrails for finance, healthcare, and public sector workloads.
Financial Services
SOX-aligned approvals and full auditability for trading, treasury, and support bots.
Healthcare
PHI-safe guardrails and zero-trust enforcement for clinical and back-office agents.
Public Sector
Policy-backed automation with strict access controls for sensitive workloads.
Trusted by teams shipping AI in production
Auditability, approvals, and enforcement without slowing down delivery.
"Faramesh gave us the approvals and auditability we needed to ship AI assistants into regulated environments."
"We wrapped every agent action with policy checks in a week. Now every exec gets a clean audit trail."
Real-time governance and visibility
Policy-as-code editor, risk dashboard, and comprehensive audit trails.
Audit Log
Drop-in policy-as-code for your stack
Use SDKs or HTTP to wrap every agent action with approvals and auditability.
Wrap any agent action with governance using the Python SDK
from faramesh import configure, submit_action
# Configure client
configure(base_url="http://localhost:8000")
# Submit action with governance
response = submit_action(
agent_id="my-agent",
tool="shell",
operation="run",
params={"cmd": "echo 'Hello Faramesh'"}
)
print(f"Status: {response['status']}")
print(f"Action ID: {response['id']}")
# Check if approval is needed
if response['status'] == 'pending':
print("Action requires approval")Works with your favorite frameworks
One-line governance for LangChain, CrewAI, AutoGen, MCP, and more.