AI Agents are being used more and more — from customer support bots, agents that automatically collect data, to code review bots. The problem is, when agents have the ability to call APIs, manipulate browsers, or handle financial transactions, who will be in control if they act incorrectly?
A real-world example: a support bot unilaterally refunded $120 while the company's policy only allows a maximum of $50. Or an agent scraping data accidentally accessed the internal system. Or an automation bot automatically entered a password into a strange banking page.
These situations are not hypothetical — they are happening at companies deploying AI agents into production.
What can SafetyOps do?
Set the rules for the agent — Clearly define which tools the agent can use, which tools are prohibited, which domains can be accessed, and limit spending for each run. Everything is checked automatically before each action of the agent.
Human final approval — When the agent hits a risk threshold, the system automatically stops and sends an approval request. The reviewer sees the full context: which agent, what it is doing, and why it is blocked. Approve and it continues, deny and it stops completely.
AI assesses risk — Each action is analyzed by Google Gemini and given a risk score from 0-100, along with explanations in natural language. Combining hard rules + AI ensures no exception cases are missed.
Monitor browser agents — If the agent operates in the browser, each click, text entry, and page navigation is recorded and assessed. If it detects the agent entering credentials into a strange form, it blocks it immediately.
Log everything — All actions have an audit trail. Serving compliance, handling incidents, and answering the question "what happened at 3 AM?"
Easy integration — Provides standard REST API, any framework (LangChain, CrewAI, AutoGen, or custom-built) can be attached.
Who should use it?
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