Stop AI agents from doing the wrong thing at the right time
Safentic enforces company policies on AI agents at runtime, blocking actions that violate rules or exceed their role.
The problem
AI Agents Act Faster Than Teams Can Control
Once deployed, LLM agents can execute tools, make API calls, and trigger workflows without human oversight. These actions can be unsafe, non-compliant, or irreversible. Traditional monitoring only catches problems after damage is done.
Unsafe actions
Agents can call the wrong tools or operate on sensitive data
No enforcement layer
There's nothing stopping execution once a decision is made
Post-hoc visibility
Logs and alerts arrive after failures, not before
Install SDK
Install Safentic in Seconds
Drop Safentic into your agent stack to intercept actions, enforce policies, and log every decision, without changing your code.
pip install safenticDefine allowed, blocked, or reviewed actions with a simple YAML policy.
tools:
delete_file:
rules:
- type: llm_verifier
instruction: "Is this action safe to execute?"
fields: [path]
action: block
- type: deterministic
match: "/tmp/*"
action: allowPolicies are enforced at runtime, before any tool runs.
Runtime pipeline
How Safentic Works
Safentic runs in the execution path of AI agents, enforcing safety decisions before tools are executed.
Agent forms intent
Your LLM agent decides to invoke a tool as part of its normal reasoning and workflow.
Safentic enforces safety after intent is formed and before execution begins, the only point where unsafe actions can still be reliably stopped.
Core capabilities
Built For Production Safety
Safentic is designed around the real failure modes of agentic systems, not demos.
Runtime enforcement
Actions are evaluated and enforced before execution, ensuring unsafe behavior never reaches production.
Policy-driven control
Define safety rules with deterministic checks and LLM verification, no agent code changes required.
Auditability by default
Every decision is logged with full context for compliance, debugging, and post-incident review.
Who it's for
Where Safentic Fits
Safentic is used by teams deploying autonomous systems where actions must be controlled, explained, and auditable at runtime.
Platform & Infrastructure Engineers
Problem: LLM agents can trigger unsafe or irreversible tool calls without a clear enforcement layer.
Outcome: Safentic enforces policies at the point of execution, giving engineers control without touching agent logic.
AI Agent Developers
Problem: Agent behavior is unpredictable, making production risky and slow.
Outcome: Developers ship faster by relying on runtime enforcement instead of manual guardrails.
Security & Compliance Teams
Problem: There's no reliable record of what actions agents took or why.
Outcome: Every decision is logged with full context for audits, reviews, and investigations.
Teams in Regulated Industries
Problem: Traditional monitoring only catches issues after damage is done.
Outcome: Safentic blocks or gates unsafe actions before they run, cutting both operational and compliance risk.
Frequently Asked Questions
Everything you need to know about getting started with Safentic.